{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><i>Peter Norvig<br>2012<br>Updated 2020</i></div>\n",
    "\n",
    "# Poker: Ranking Hands, etc.\n",
    "\n",
    "\n",
    "The [rules for poker hands](https://en.wikipedia.org/wiki/List_of_poker_hands) are complex, but it is an interesting exercise to write a program to rank poker hands&mdash;to determine if one is higher or lower than another&mdash;as I did in my [Udacity 212](https://www.udacity.com/course/design-of-computer-programs--cs212) course after making a [cheesy video](https://www.youtube.com/watch?v=PI8Fo1vzUPM) with David Evans.  We'll cover only the ranking part of poker, not the betting part. \n",
    "\n",
    "Some key concepts:\n",
    "\n",
    "- **Card**: A card will be represented as a two character string, like `'9c'`, where the first character is the **rank** and the second is the **suit**. The ranks are `23456789TJQKA` in ascending order of value and the suits are `'cdhs'` for clubs, diamonds, hearts, and spades; all suits have equal value in poker. I thought about using the Unicode characters `'♣︎♢♡♠︎'`, but they are hard to find on the keyboard. I also thought of using `(10, 0)` instead of `'Tc'`; the former might allow for a bit faster code, but the later is easier to look at when debugging.\n",
    "- **Hand**: A hand is a collection of five cards: `['3s', '3c', 'As', 'Ks', 'Qs']`\n",
    "- **Type**: A hand has a ranking type; these are (in highest to lowest order): five of a kind, straight flush, four of a kind, full house, flush, straight, three of a kind, two pair, one pair, high card. (Five of a kind can only be made in games with wild cards.)\n",
    "- **Group**: Two or more  cards with the same rank: a pair, a three-of-a-kind, etc.\n",
    "- **Winning**: If two hands have different types, the higher type wins. If they have the same type, a **tiebreaker** is needed. \n",
    "- **Tiebreaker**: For example, the tiebreaker for \"straight\" is the rank of the highest card; a ten-high straight beats a nine-high straight. The tiebreaker for \"three of a kind\" is the rank of the three-of-a-kind group.\n",
    "- **Ranking**: To determine which hand wins we could have a comparison function, `compare_ranks(hand1, hand2)`. But it is simpler to compare ranks with `ranking(hand) < ranking(hand2)`. The function `ranking` returns a tuple that encompasses the type and tiebreaker.  Note that the word **ranking** refers to the value of a *hand*, but **rank** refers to the value of a *card*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ranking and Integer Partitions: `ranking0`\n",
    "\n",
    "There is a curious correspondence between the seven types of poker hands that involve groups (that is, all the types except the ones for straights and flushes) and the seven **[integer partitions](https://en.wikipedia.org/wiki/Partition_(number_theory))** of the number 5 (remember, there are 5 cards in a hand). \n",
    "\n",
    "Consider this table of ranking types, where the straights and flushes are omitted:\n",
    "\n",
    "\n",
    "| Type|  Example &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|Partitions |\n",
    "|-|---|---|\n",
    "| Five of a kind |`As Ac Ah Ad Aj`| (5,) |\n",
    "| Four of a kind |`7s 7c 7d 2d 7h`|  (4, 1)|\n",
    "| Full house | `8h 9c 8d 8c 9h`|     (3, 2)|\n",
    "| Three of a kind | `Ts Tc Th 9s 7c`| (3, 1, 1)|\n",
    "| Two pair |  `Ts Tc 9s 9c 7h`|       (2, 2, 1)  |\n",
    "| One pair |  `3s 3c As Ks Qs`|          (2, 1, 1, 1)|\n",
    "| High card | `2s 4s 5s 6s 7h`|         (1, 1, 1, 1, 1)|\n",
    "\n",
    "The types are sorted from highest to lowest, and the partitions are also sorted in lexicographic order from highest to lowest. The correspondence is that, for example, `(3, 2)` means \"three cards of one rank and two cards of another rank\", which is the definition of a full house. \n",
    "\n",
    "Let's get some preliminaries out of the way so we can start to program a solution for ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from statistics  import mean\n",
    "from itertools   import combinations, permutations, product\n",
    "from functools   import lru_cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Types\n",
    "Card = str              # A card is a str of length 2: '7s'\n",
    "Hand = (list, tuple)    # A hand is 5 cards in either a list or a tuple\n",
    "\n",
    "# Functions and Objects\n",
    "join    = ' '.join        # Function to join cards together into one string\n",
    "cards   = str.split       # Function to split a string apart into a list of cards\n",
    "rankstr = '23456789TJQKA' # Card ranks in ascending order\n",
    "\n",
    "def rank(card) -> int: return rankstr.index(card[0]) + 2\n",
    "def suit(card) -> str: return card[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `collections.Counter` does most of the work of finding  groups and thus partitions. It tells us that the following full house has three 8s and two 9s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({8: 3, 9: 2})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hand = cards('8h 9c 8d 8c 9h')\n",
    "\n",
    "Counter(map(rank, hand))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us all the information we need; the problem is that it is not in the right format to compare rankings. We can't  do `counter1 < counter2` because Counters do not support comparison. But we can reformat the information into a tuple consisting of two components: \n",
    " 1. The **type** of hand, full house, which is denoted by the partition `(3, 2)`.\n",
    " 2. The **tiebreakers**: among all full houses, ties go to the highest three-of-a-kind rank, and if those are the same, then the highest pair rank. So the tiebreaker for this hand would be `(8, 9)`. (Even though 9 > 8, the 8 comes first because the three-of-a-kind is more important than the pair.)\n",
    "\n",
    "Thus the complete ranking for this hand is the tuple `((3, 2), (8, 9))`. The same information as in the Counter, but in the proper order for sorting.\n",
    "\n",
    "So (ignoring straights and flushes for now) here's how we do  `ranking`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ranking0(hand) -> tuple:\n",
    "    \"\"\"Return a (type, tiebreaker) tuple indicating how high the hand ranks.\"\"\"\n",
    "    counts = Counter(map(rank, hand))\n",
    "    groups = sorted(((counts[r], r) for r in counts), reverse=True)\n",
    "    return tuple(zip(*groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the process step by step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({8: 3, 9: 2})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = Counter(map(rank, hand))\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 8), (2, 9)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = sorted(((counts[r], r) for r in counts), reverse=True)\n",
    "groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 2), (8, 9))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(zip(*groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this final result is the **matrix transpose** (switching rows and columns) of  `groups`. In Python the transpose of `m` is `zip(*m)`.\n",
    "\n",
    "Below we see that the eights-over-nines full house beats a six-over-tens full house (it doesn't matter that 10 > 9; what matters is 8 > 6):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hand2 = cards('6h 6c 6d Tc Th')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 2), (8, 9))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking0(hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 2), (6, 10))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking0(hand2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking0(hand) > ranking0(hand2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Complete Ranking: `ranking1`\n",
    "\n",
    "It is a cute mathematical result that poker rankings correspond to the integer partitions of 5, but the correspondence does not account for straights and flushes. Still, we can salvage the approach by inventing \"type tuples\" for straight, flush, and straight flush that are not partitions but are in the correct sort order with respect to the seven actual partitions of 5. There are many possible choices; my choices, shown in **bold** below, are to look at the type one row below and add 1 to the last digit: one better than `(3, 1, 1)` is `(3, 1, 2)`; one better than `(4, 1)` is `(4, 2)`.\n",
    "\n",
    "\n",
    "| Type| # | Example &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|Type tuple |\n",
    "|-|--|---|---|\n",
    "| Five of a kind |9|`As Ac Ah Ad Aj`| (5,) |\n",
    "| Straight flush  |8|`As Ks Qs Ts Js`| **(4, 2)**|\n",
    "| Four of a kind |7|`7s 7c 7d 2d 7h`|  (4, 1)|\n",
    "| Full house |6| `8h 9c 8d 8c 9h`|     (3, 2)|\n",
    "| Flush |5| `8c Kc Qc Jc Tc`|  **(3, 1, 3)**        |\n",
    "| Straight |4| `Kc Qh Jd Th 9c`| **(3, 1, 2)**      |\n",
    "| Three of a kind |3| `Ts Tc Th 9s 7c`| (3, 1, 1)|\n",
    "| Two pair |2|  `Ts Tc 9s 9c 7h`|       (2, 2, 1)  |\n",
    "| One pair |1|  `3s 3c As Ks Qs`|          (2, 1, 1, 1)|\n",
    "| High card |0| `2s 4s 5s 6s 7h`|         (1, 1, 1, 1, 1)|\n",
    "\n",
    "It is easy to code this up; the function `special_type` handles  flushes and straights, and `rankings` uses the special type or the regular type. Another complication is that in poker aces are not always high: If we have a 5-4-3-2-A hand, the ace serves as a `1` rather than a `14`; we adjust `ranks` accordingly in `rankings`. To test for a flush, we look at the collection of suits and ask if they are all `the_same`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ranking1(hand) -> tuple:\n",
    "    \"\"\"Return a value indicating how high the hand ranks.\"\"\"\n",
    "    counts = Counter(map(rank, hand))\n",
    "    groups = sorted(((counts[r], r) for r in counts), reverse=True)\n",
    "    type, ranks = zip(*groups)\n",
    "    if ranks == (14, 5, 4, 3, 2):\n",
    "        ranks = (5, 4, 3, 2, 1)\n",
    "    type = special_type(hand, ranks) or type\n",
    "    return (type, ranks)\n",
    "\n",
    "def special_type(hand, ranks) -> tuple:\n",
    "    \"\"\"For a flush or straight, return a tuple comparable with `type` in `ranking`.\"\"\"\n",
    "    straight = len(ranks) == 5 and max(ranks) - min(ranks) == 4\n",
    "    flush    = the_same(map(suit, hand))\n",
    "    return ((4, 2)    if straight and flush else \n",
    "            (3, 1, 3) if flush else \n",
    "            (3, 1, 2) if straight else ())\n",
    "\n",
    "def the_same(things) -> bool: \n",
    "    \"\"\"Are all the things actually the same?\"\"\"\n",
    "    return len(set(things)) <= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Poker Hands\n",
    "\n",
    "Below is a list of `hands`, with four examples of each **type**, all in descending ranking order (i.e. best to worst).  The `test` function asserts that:\n",
    "- The `ranking` function agrees with this ordering.\n",
    "- The order of cards within a hand does not matter—every permutation of cards is ranked the same. \n",
    "- Swapping the suits around (e.g. swapping every spade with every heart) does not change the ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "hands = [cards(h) for h in (           \n",
    "    'As Ac Ah Ad Ad', 'Kh Kd Ks Kc Kh', '3h 3s 3d 3c 3c', '2s 2c 2d 2h 2h', # 5 of a kind     \n",
    "    'As Ks Qs Ts Js', 'Kc Qc Jc Tc 9c', '6d 5d 4d 3d 2d', '5h 4h 3h 2h Ah', # straight flush  \n",
    "    'As Ac Ad Ah 2s', '7s 7c 7d 2d 7h', '6s 6c 6d 6h 9s', 'As 5h 5c 5d 5s', # four of a kind \n",
    "    'Th Tc Td 5h 5c', '9h 9c 9d 8c 8h', '6h 6c 6d Tc Th', '5c 5d 5s As Ah', # full house\n",
    "    'As 2s 3s 4s 6s', 'Kc Qc Jc Tc 2c', 'Qc Jc Tc 9c 7c', '4h 5h 6h 7h 9h', # flush\n",
    "    'As Kd Qc Td Jh', 'Kc Qh Jd Th 9c', '6c 5d 4h 3s 2s', 'As 2d 3c 4h 5s', # straight\n",
    "    'As Ac Ad 2h 3h', 'Ts Tc Th 9s 8c', 'Ts Tc Th 9s 7c', '9h 9s 9d Ah Kh', # three of a kind\n",
    "    'Ts Tc 5s 5c 8h', 'Ts Tc 5s 5c 7h', '9s 9c 8s 8c As', '3s 3c 2s 2d Ah', # two pair \n",
    "    'As Ac 4c 5s 6s', '4s 4c As Ks Qs', '4h 4d Kh Qd Jd', '2d 2c Ad Kd Qd', # pair \n",
    "    'Ah 3s 4s 5s 6s', 'Kh Qh Jh Th 8d', '7d 2s 4s 5s 6s', '7h 6s 5d 3s 2d', # high card\n",
    "         )]\n",
    "\n",
    "def test(ranking, hands=hands) -> bool:\n",
    "    \"\"\"Test that `ranking` preserves order of `hands`, and that permuting cards is irrelevant.\"\"\"\n",
    "    assert hands == sorted(hands, key=ranking, reverse=True)\n",
    "    trans = str.maketrans('shdc', 'hscd')\n",
    "    for hand in hands: \n",
    "        assert the_same(ranking(h) for h in permutations(hand))\n",
    "        assert the_same([ranking(hand), ranking([c.translate(trans) for c in hand])])\n",
    "    return len(hands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test(ranking1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the rankings look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'As Ac Ah Ad Ad': ((5,), (14,)),\n",
       " 'Kh Kd Ks Kc Kh': ((5,), (13,)),\n",
       " '3h 3s 3d 3c 3c': ((5,), (3,)),\n",
       " '2s 2c 2d 2h 2h': ((5,), (2,)),\n",
       " 'As Ks Qs Ts Js': ((4, 2), (14, 13, 12, 11, 10)),\n",
       " 'Kc Qc Jc Tc 9c': ((4, 2), (13, 12, 11, 10, 9)),\n",
       " '6d 5d 4d 3d 2d': ((4, 2), (6, 5, 4, 3, 2)),\n",
       " '5h 4h 3h 2h Ah': ((4, 2), (5, 4, 3, 2, 1)),\n",
       " 'As Ac Ad Ah 2s': ((4, 1), (14, 2)),\n",
       " '7s 7c 7d 2d 7h': ((4, 1), (7, 2)),\n",
       " '6s 6c 6d 6h 9s': ((4, 1), (6, 9)),\n",
       " 'As 5h 5c 5d 5s': ((4, 1), (5, 14)),\n",
       " 'Th Tc Td 5h 5c': ((3, 2), (10, 5)),\n",
       " '9h 9c 9d 8c 8h': ((3, 2), (9, 8)),\n",
       " '6h 6c 6d Tc Th': ((3, 2), (6, 10)),\n",
       " '5c 5d 5s As Ah': ((3, 2), (5, 14)),\n",
       " 'As 2s 3s 4s 6s': ((3, 1, 3), (14, 6, 4, 3, 2)),\n",
       " 'Kc Qc Jc Tc 2c': ((3, 1, 3), (13, 12, 11, 10, 2)),\n",
       " 'Qc Jc Tc 9c 7c': ((3, 1, 3), (12, 11, 10, 9, 7)),\n",
       " '4h 5h 6h 7h 9h': ((3, 1, 3), (9, 7, 6, 5, 4)),\n",
       " 'As Kd Qc Td Jh': ((3, 1, 2), (14, 13, 12, 11, 10)),\n",
       " 'Kc Qh Jd Th 9c': ((3, 1, 2), (13, 12, 11, 10, 9)),\n",
       " '6c 5d 4h 3s 2s': ((3, 1, 2), (6, 5, 4, 3, 2)),\n",
       " 'As 2d 3c 4h 5s': ((3, 1, 2), (5, 4, 3, 2, 1)),\n",
       " 'As Ac Ad 2h 3h': ((3, 1, 1), (14, 3, 2)),\n",
       " 'Ts Tc Th 9s 8c': ((3, 1, 1), (10, 9, 8)),\n",
       " 'Ts Tc Th 9s 7c': ((3, 1, 1), (10, 9, 7)),\n",
       " '9h 9s 9d Ah Kh': ((3, 1, 1), (9, 14, 13)),\n",
       " 'Ts Tc 5s 5c 8h': ((2, 2, 1), (10, 5, 8)),\n",
       " 'Ts Tc 5s 5c 7h': ((2, 2, 1), (10, 5, 7)),\n",
       " '9s 9c 8s 8c As': ((2, 2, 1), (9, 8, 14)),\n",
       " '3s 3c 2s 2d Ah': ((2, 2, 1), (3, 2, 14)),\n",
       " 'As Ac 4c 5s 6s': ((2, 1, 1, 1), (14, 6, 5, 4)),\n",
       " '4s 4c As Ks Qs': ((2, 1, 1, 1), (4, 14, 13, 12)),\n",
       " '4h 4d Kh Qd Jd': ((2, 1, 1, 1), (4, 13, 12, 11)),\n",
       " '2d 2c Ad Kd Qd': ((2, 1, 1, 1), (2, 14, 13, 12)),\n",
       " 'Ah 3s 4s 5s 6s': ((1, 1, 1, 1, 1), (14, 6, 5, 4, 3)),\n",
       " 'Kh Qh Jh Th 8d': ((1, 1, 1, 1, 1), (13, 12, 11, 10, 8)),\n",
       " '7d 2s 4s 5s 6s': ((1, 1, 1, 1, 1), (7, 6, 5, 4, 2)),\n",
       " '7h 6s 5d 3s 2d': ((1, 1, 1, 1, 1), (7, 6, 5, 3, 2))}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{join(h): ranking1(h) for h in hands}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative Definition: `ranking2`\n",
    "\n",
    "If you think dealing with integer partitions of 5 was a little too abstract, and/or that fabricating `(3, 1, 3)` as the type for a flush was a little too *ad hoc*, then here's an alternative ranking function, `ranking2`, that you might find more straightforward. Instead of type tuples, it uses the integers 9 to 0 for types, and then does tiebreakers in the same way as `ranking`.\n",
    "\n",
    "The one tricky part is the use of `kinds/kind`: for a two-pair hand `8h 9c 8d 9d 4h`, `kinds` will be the list `[[], [4], [9, 8], [], [], []]`, and `kinds[2]` is `[9, 8]` (meaning: what ranks have 2 of a kind? 9 and 8, with the highest rank always first), and `kind(1)` is `4` (meaning: what rank has one of a kind? 4). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ranking2(hand) -> tuple:\n",
    "    \"\"\"Return a value indicating how high the hand ranks.\"\"\"\n",
    "    ranks = sorted(map(rank, hand), reverse=True)\n",
    "    if ranks == [14, 5, 4, 3, 2]:\n",
    "        ranks = [5, 4, 3, 2, 1]\n",
    "    straight = len(set(ranks)) == 5 and max(ranks) - min(ranks) == 4\n",
    "    flush    = the_same(map(suit, hand))\n",
    "    kinds    = [[] for n in range(6)]\n",
    "    kind     = lambda n: kinds[n][0]\n",
    "    for r in sorted(set(ranks), reverse=True):\n",
    "        kinds[ranks.count(r)].append(r)\n",
    "    return ((9, max(ranks))         if kinds[5] else\n",
    "            (8, max(ranks))         if straight and flush else\n",
    "            (7, kind(4), kind(1))   if kinds[4] else\n",
    "            (6, kind(3), kind(2))   if kinds[3] and kinds[2] else\n",
    "            (5, *ranks)             if flush else\n",
    "            (4, max(ranks))         if straight else\n",
    "            (3, kind(3), *kinds[1]) if kinds[3] else\n",
    "            (2, *kinds[2], kind(1)) if len(kinds[2]) == 2 else\n",
    "            (1, kind(2), *kinds[1]) if kinds[2] else\n",
    "            (0, *ranks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ranking2` passes the tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test(ranking2, hands)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are what the `ranking2` rankings look like&mdash;a little more concise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'As Ac Ah Ad Ad': (9, 14),\n",
       " 'Kh Kd Ks Kc Kh': (9, 13),\n",
       " '3h 3s 3d 3c 3c': (9, 3),\n",
       " '2s 2c 2d 2h 2h': (9, 2),\n",
       " 'As Ks Qs Ts Js': (8, 14),\n",
       " 'Kc Qc Jc Tc 9c': (8, 13),\n",
       " '6d 5d 4d 3d 2d': (8, 6),\n",
       " '5h 4h 3h 2h Ah': (8, 5),\n",
       " 'As Ac Ad Ah 2s': (7, 14, 2),\n",
       " '7s 7c 7d 2d 7h': (7, 7, 2),\n",
       " '6s 6c 6d 6h 9s': (7, 6, 9),\n",
       " 'As 5h 5c 5d 5s': (7, 5, 14),\n",
       " 'Th Tc Td 5h 5c': (6, 10, 5),\n",
       " '9h 9c 9d 8c 8h': (6, 9, 8),\n",
       " '6h 6c 6d Tc Th': (6, 6, 10),\n",
       " '5c 5d 5s As Ah': (6, 5, 14),\n",
       " 'As 2s 3s 4s 6s': (5, 14, 6, 4, 3, 2),\n",
       " 'Kc Qc Jc Tc 2c': (5, 13, 12, 11, 10, 2),\n",
       " 'Qc Jc Tc 9c 7c': (5, 12, 11, 10, 9, 7),\n",
       " '4h 5h 6h 7h 9h': (5, 9, 7, 6, 5, 4),\n",
       " 'As Kd Qc Td Jh': (4, 14),\n",
       " 'Kc Qh Jd Th 9c': (4, 13),\n",
       " '6c 5d 4h 3s 2s': (4, 6),\n",
       " 'As 2d 3c 4h 5s': (4, 5),\n",
       " 'As Ac Ad 2h 3h': (3, 14, 3, 2),\n",
       " 'Ts Tc Th 9s 8c': (3, 10, 9, 8),\n",
       " 'Ts Tc Th 9s 7c': (3, 10, 9, 7),\n",
       " '9h 9s 9d Ah Kh': (3, 9, 14, 13),\n",
       " 'Ts Tc 5s 5c 8h': (2, 10, 5, 8),\n",
       " 'Ts Tc 5s 5c 7h': (2, 10, 5, 7),\n",
       " '9s 9c 8s 8c As': (2, 9, 8, 14),\n",
       " '3s 3c 2s 2d Ah': (2, 3, 2, 14),\n",
       " 'As Ac 4c 5s 6s': (1, 14, 6, 5, 4),\n",
       " '4s 4c As Ks Qs': (1, 4, 14, 13, 12),\n",
       " '4h 4d Kh Qd Jd': (1, 4, 13, 12, 11),\n",
       " '2d 2c Ad Kd Qd': (1, 2, 14, 13, 12),\n",
       " 'Ah 3s 4s 5s 6s': (0, 14, 6, 5, 4, 3),\n",
       " 'Kh Qh Jh Th 8d': (0, 13, 12, 11, 10, 8),\n",
       " '7d 2s 4s 5s 6s': (0, 7, 6, 5, 4, 2),\n",
       " '7h 6s 5d 3s 2d': (0, 7, 6, 5, 3, 2)}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{join(h): ranking2(h) for h in hands}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timing\n",
    "\n",
    "Below we see that `ranking2` is about 25% faster than `ranking1`, so use `ranking2` when speed is important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.5 ms ± 381 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "19.6 ms ± 432 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit test(ranking1,  hands)\n",
    "%timeit test(ranking2, hands)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Winning\n",
    "\n",
    "It is tempting to define a function `winner(hands) -> Hand` to see who wins a round, and we will do that, for convenience. But in poker if two hands are exactly the same except for suits, then they tie. So to be more precise we will also define `winners(hand)` to return a *set* of winners (which in most cases will have only one member).\n",
    "\n",
    "Note that **ranking** is the fundamental idea, and **winning** is determined by ranking. Some programmers try to answer the question of whether one hand is better than another by defining `better(hand1, hand2)`. They quickly find that it is hopelessly complex to be considering two hands at the same time, asking things like \"this hand has three of a kind; does the other hand have anything better?\"  It is much simpler to compare rankings. \n",
    "\n",
    "Also note that using the word **ranking** rather than **scoring** is deliberate. If we were doing scoring, it would make sense to ask for an average score for a set of hands. With ranking, we can sort hands from best to worst, but it makes no sense to say a straight (type 4) and a straight flush (type 8) average out to a full house (type 6).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def winner(hands) -> Hand: \n",
    "    \"\"\"One of the winning hands (there might be others).\"\"\"\n",
    "    return max(hands, key=ranking2)\n",
    "\n",
    "def winners(hands) -> set:\n",
    "    \"\"\"The set of hands with the best ranking.\"\"\"\n",
    "    best = max(map(ranking2, hands))\n",
    "    return {h for h in hands if ranking2(h) == best}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Type Names\n",
    "\n",
    "So far, the names of the types (e.g. \"full house\") have appeared only in prose, not in the code. Let's fix that. The hand type returned by `ranking2` is an integer ranging from 0 to 9 that can be used as an index into a tuple of `type_names`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "types = range(10)\n",
    "\n",
    "type_names = ('high card', 'one pair', 'two pair', 'three of a kind', 'straight',\n",
    "  'flush', 'full house', 'four of a kind', 'straight flush', 'five of a kind')\n",
    "\n",
    "def type_name(hand) -> str: return type_names[ranking2(hand)[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'As Ac Ah Ad Ad': 'five of a kind',\n",
       " 'Kh Kd Ks Kc Kh': 'five of a kind',\n",
       " '3h 3s 3d 3c 3c': 'five of a kind',\n",
       " '2s 2c 2d 2h 2h': 'five of a kind',\n",
       " 'As Ks Qs Ts Js': 'straight flush',\n",
       " 'Kc Qc Jc Tc 9c': 'straight flush',\n",
       " '6d 5d 4d 3d 2d': 'straight flush',\n",
       " '5h 4h 3h 2h Ah': 'straight flush',\n",
       " 'As Ac Ad Ah 2s': 'four of a kind',\n",
       " '7s 7c 7d 2d 7h': 'four of a kind',\n",
       " '6s 6c 6d 6h 9s': 'four of a kind',\n",
       " 'As 5h 5c 5d 5s': 'four of a kind',\n",
       " 'Th Tc Td 5h 5c': 'full house',\n",
       " '9h 9c 9d 8c 8h': 'full house',\n",
       " '6h 6c 6d Tc Th': 'full house',\n",
       " '5c 5d 5s As Ah': 'full house',\n",
       " 'As 2s 3s 4s 6s': 'flush',\n",
       " 'Kc Qc Jc Tc 2c': 'flush',\n",
       " 'Qc Jc Tc 9c 7c': 'flush',\n",
       " '4h 5h 6h 7h 9h': 'flush',\n",
       " 'As Kd Qc Td Jh': 'straight',\n",
       " 'Kc Qh Jd Th 9c': 'straight',\n",
       " '6c 5d 4h 3s 2s': 'straight',\n",
       " 'As 2d 3c 4h 5s': 'straight',\n",
       " 'As Ac Ad 2h 3h': 'three of a kind',\n",
       " 'Ts Tc Th 9s 8c': 'three of a kind',\n",
       " 'Ts Tc Th 9s 7c': 'three of a kind',\n",
       " '9h 9s 9d Ah Kh': 'three of a kind',\n",
       " 'Ts Tc 5s 5c 8h': 'two pair',\n",
       " 'Ts Tc 5s 5c 7h': 'two pair',\n",
       " '9s 9c 8s 8c As': 'two pair',\n",
       " '3s 3c 2s 2d Ah': 'two pair',\n",
       " 'As Ac 4c 5s 6s': 'one pair',\n",
       " '4s 4c As Ks Qs': 'one pair',\n",
       " '4h 4d Kh Qd Jd': 'one pair',\n",
       " '2d 2c Ad Kd Qd': 'one pair',\n",
       " 'Ah 3s 4s 5s 6s': 'high card',\n",
       " 'Kh Qh Jh Th 8d': 'high card',\n",
       " '7d 2s 4s 5s 6s': 'high card',\n",
       " '7h 6s 5d 3s 2d': 'high card'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{join(h): type_name(h) for h in hands}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Poker Variants\n",
    "\n",
    "There are many variants of poker. In some  variants a player makes a hand by combining face-down private cards that they hold, called **hole cards**, with face-up shared cards in the middle of the table called **community cards**. Let's look at a few variants from the point of view of card ranking. Again, we'll ignore the betting parts.\n",
    "\n",
    "# Texas Hold 'Em  Poker\n",
    "\n",
    "In Texas Hold 'Em, one of the most popular games, each player has two  hole cards, and everyone shares five community cards. A player can choose any subset of the seven cards for their hand. \n",
    "\n",
    "Finding the best hand from among the hole and community cards is straightforward: just try every combination and choose a hand with the maximum ranking (that's what the function `winner` does):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def texas_best(hole, community) -> Hand:\n",
    "    \"\"\"Find the best hand from any selection of 2 hole and 5 community cards.\"\"\"\n",
    "    assert len(hole) == 2 and len(community) == 5\n",
    "    return winner(combinations(hole + community, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A player can make a full house with the following hole cards and community cards:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('As', '7d', 'Ac', 'Ah', '7c')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texas_best(cards('As 7d'), cards('Ac 8d 9d Ah 7c'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Omaha Hold 'Em Poker\n",
    "\n",
    "In Omaha Hold 'Em, players get four hole cards, and they have to use exactly two of them, plus three of the five community cards, to make their hand. Not quite as straightforward to find the best hand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def omaha_best(hole, community) -> Hand:\n",
    "    \"\"\"Find the best hand using 2 of 4 hole cards and 3 of 5 community cards.\"\"\"\n",
    "    assert len(hole) == 4 and len(community) == 5\n",
    "    return winner(h2 + c3 for h2 in combinations(hole, 2) \n",
    "                          for c3 in combinations(community, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('As', '7d', 'Ac', 'Ah', '7c')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omaha_best(cards('As 7d 6d 5d'), cards('Ac 8d 9d Ah 7c'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following hand, the hole plus community cards include a royal straight flush and a fives-over-tens full house. But neither of these hands can be made because the hand must have exactly two hole cards, so we end up with three fives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Ad', '5d', 'Jd', '5c', '5h')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omaha_best(cards('Ad Kd Qd 5d'), cards('Jd Td Tc 5c 5h'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-D Poker\n",
    "\n",
    "In this novel variant, due to Bram Cohen, there are two hole cards and 15 community cards arranged in three sets of five. Players must use their two hole cards, plus exactly one card from each of the three sets of community cards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def threeD_best(hole, community) -> Hand:\n",
    "    \"\"\"Find the best hand from the 2 hole cards plus 1 each from \n",
    "    the first 5, middle 5, and last 5 of the 15 community cards.\"\"\"\n",
    "    assert len(hole) == 2 and len(community) == 15\n",
    "    A, B, C = community[0:5], community[5:10], community[10:15]\n",
    "    return winner([*hole, *c3] for c3 in product(A, B, C))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example below, the player has a queen hole card and there are two queen community cards, but they are in the same set, so the player could only get one of them. It turns out that it is better to give up on the idea of three queens and take two kings and a 4 instead, making two pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Qh', '4s', 'Kd', '4h', 'Kc']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threeD_best(cards('Qh 4s'), cards('Qs Qc Kd Ts Ah    5d 4h 7s 9c 6s    5h 7c Kc Kh 9h'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joker Poker\n",
    "\n",
    "Sometimes in friendly house games, a joker or two is added to the deck. A joker can serve as any card the player wants it to be, including a card already in their hand (that's how you get five-of-a-kind)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "joker  ='**'\n",
    "deck   = [r + s for r in rankstr for s in 'cdhs'] # Regular Deck of 52 cards\n",
    "deck54 = [joker, joker, *deck] # Deck of 54 cards including two jokers\n",
    "\n",
    "def joker_best(hand) -> Hand:\n",
    "    \"\"\"Find the best hand by replacing any jokers with any other card.\"\"\"\n",
    "    replacements = [deck if card == joker else [card] for card in hand]\n",
    "    return winner(product(*replacements))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('8d', '8c', '7d', '6d', '8s')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joker_best(cards('8d ** 7d 6d 8s')) # One Joker used to make three of a kind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('8d', '9d', '7d', '6d', 'Td')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joker_best(cards('8d ** 7d 6d **')) # Two Jokers used to make a straight flush"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the poker variants we have seen so far, even joker poker, were solved by finding a hand with maximum ranking out of all the possible hands that could be made with community cards or jokers. There was always a clear-cut way to do this, and one definitive best hand. Next we will see a variant where that is no longer true.\n",
    "\n",
    "# Draw Poker\n",
    "\n",
    "In draw poker players are given the option to **discard** one or more card(s) and **replace** them with new one(s) drawn from the deck. But unlike in joker poker where you get to choose the replacement, in draw poker you're at the mercy of the luck of the draw. \n",
    "\n",
    "In draw poker there is no **best hand** that you can make with certainty; instead we need to find the **best action** (which card to discard), with the understanding that the action will sometimes work (we hit that straight) and sometimes not. The best action is determined by the best **average** over all possible outcomes of the action (i.e. all possible draws of cards).\n",
    "\n",
    "We could try to find the action that maximizes the average type number of the resulting hands. But that's not quite right: there's a big advantage in going from type 1 (one pair) to type 3 (three of a kind), because that very often makes the difference between losing and winning. There is less of an advantage in going from type 7 (four of a kind) to type 9 (five of a kind) because the four of a kind will win most of the time anyway. \n",
    "\n",
    "# Win Probability\n",
    "\n",
    "What we really want to do is **maximize the expected probability of winning**.  The probability of winning depends on many things: the number of other players, the community cards if any, the other rules of the game (e.g. are draws or jokers allowed), the skill of the other players, the hints that the other players have given about their cards through their bets and physical tells, etc.\n",
    "\n",
    "We'll simplify by ignoring most of these factors.  We'll assume **heads up** poker, in which there are only two players. A hand's probability of winning is proportional to the percentage of hands that it is better than, out of all hands. Wikipedia [says](https://en.wikipedia.org/wiki/Poker_probability)  that 50.1% of all hands are \"high card\", so we might say that a \"high card\" hand has a 50.1% probability of winning. But that can't be the full story, because an ace high card has a much better chance of winning than a 7 high card. So we'll estimate that the probability of a hand winning is a linear interpolation, based on the value of the high card in the hand, between the cumulative probability of the hand's type and the cumulative probability of one type lower. (This assumption is wrong, again, because the relationship is not exactly linear (e.g. the lowest possible high card for type 0 is actually a 7, because otherwise the hand would be a straight) and because non-high cards also come into play, but maybe it is a close enough approximation.) I'll use Wikipedia's [Frequency of 5-card poker hands](https://en.wikipedia.org/wiki/Poker_probability) to define `deal_win_P`, the cummulative probabilities keyed by hand type numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cumsum(numbers, normalize=True) -> list:\n",
    "    \"\"\"cumsum(numbers)[i] gives the cumulative sum of the first n numbers, \n",
    "    optionally normalized to sum to 1.\"\"\"\n",
    "    denom = sum(numbers) if normalize else 1\n",
    "    return [sum(numbers[:n]) / denom for n in range(len(numbers) + 1)]\n",
    "\n",
    "deal_win_P = cumsum([1302540, 1098240, 123552, 54912, 10200, 5108, 3744, 624, 40, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.5011773940345369,\n",
       " 0.9237464216455813,\n",
       " 0.9712854372518238,\n",
       " 0.992413888632376,\n",
       " 0.9963385354141656,\n",
       " 0.9983039369593991,\n",
       " 0.9997445131898913,\n",
       " 0.9999846092283067,\n",
       " 1.0,\n",
       " 1.0]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deal_win_P "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This says that 50.1% of all random hands have the type \"high card\", 92.3% are \"high card\" or \"one pair\", 97.1% are \"two pair\" or worse, and so on. With this I can define `win_probability`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def win_probability(hand, P=deal_win_P) -> float:\n",
    "    \"\"\"Approximate win probability for a hand, given a list, P, of win probabilities for types.\"\"\"\n",
    "    type, hi, *_ =  ranking2(hand)\n",
    "    return P[type] + (hi - 1) / 13 * (P[type + 1] - P[type])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are win probabilities for all the test hands, expressed in percents. The `deal_win_P` probabilities assume no wild cards, and thus no five-of-a-kind; therefore an ace-high straight flush (royal straight flush) is the highest possible hand, and gets a 100% win probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As Ac Ah Ad Ad  100.00000%  five of a kind\n",
      "Kh Kd Ks Kc Kh  100.00000%  five of a kind\n",
      "3h 3s 3d 3c 3c  100.00000%  five of a kind\n",
      "2s 2c 2d 2h 2h  100.00000%  five of a kind\n",
      "As Ks Qs Ts Js  100.00000%  straight flush\n",
      "Kc Qc Jc Tc 9c   99.99988%  straight flush\n",
      "6d 5d 4d 3d 2d   99.99905%  straight flush\n",
      "5h 4h 3h 2h Ah   99.99893%  straight flush\n",
      "As Ac Ad Ah 2s   99.99846%  four of a kind\n",
      "7s 7c 7d 2d 7h   99.98553%  four of a kind\n",
      "6s 6c 6d 6h 9s   99.98369%  four of a kind\n",
      "As 5h 5c 5d 5s   99.98184%  four of a kind\n",
      "Th Tc Td 5h 5c   99.93013%  full house\n",
      "9h 9c 9d 8c 8h   99.91904%  full house\n",
      "6h 6c 6d Tc Th   99.88580%  full house\n",
      "5c 5d 5s As Ah   99.87472%  full house\n",
      "As 2s 3s 4s 6s   99.83039%  flush\n",
      "Kc Qc Jc Tc 2c   99.81528%  flush\n",
      "Qc Jc Tc 9c 7c   99.80016%  flush\n",
      "4h 5h 6h 7h 9h   99.75480%  flush\n",
      "As Kd Qc Td Jh   99.63385%  straight\n",
      "Kc Qh Jd Th 9c   99.60366%  straight\n",
      "6c 5d 4h 3s 2s   99.39234%  straight\n",
      "As 2d 3c 4h 5s   99.36215%  straight\n",
      "As Ac Ad 2h 3h   99.24139%  three of a kind\n",
      "Ts Tc Th 9s 8c   98.59128%  three of a kind\n",
      "Ts Tc Th 9s 7c   98.59128%  three of a kind\n",
      "9h 9s 9d Ah Kh   98.42876%  three of a kind\n",
      "Ts Tc 5s 5c 8h   95.66580%  two pair\n",
      "Ts Tc 5s 5c 7h   95.66580%  two pair\n",
      "9s 9c 8s 8c As   95.30012%  two pair\n",
      "3s 3c 2s 2d Ah   93.10601%  two pair\n",
      "As Ac 4c 5s 6s   92.37464%  one pair\n",
      "4s 4c As Ks Qs   59.86933%  one pair\n",
      "4h 4d Kh Qd Jd   59.86933%  one pair\n",
      "2d 2c Ad Kd Qd   53.36827%  one pair\n",
      "Ah 3s 4s 5s 6s   50.11774%  high card\n",
      "Kh Qh Jh Th 8d   46.26253%  high card\n",
      "7d 2s 4s 5s 6s   23.13126%  high card\n",
      "7h 6s 5d 3s 2d   23.13126%  high card\n"
     ]
    }
   ],
   "source": [
    "for h in hands:\n",
    "    print(f'{join(h)}  {win_probability(h):10.5%}  {type_name(h)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you really expect to win 50% of the time with a high card ace, or 60% of the time with a pair of fours? Not in a game with community cards, or draws, or jokers, or more than two players. But if we're asking \"*two players just got dealt five cards each; what's the probability that my five cards are better than the others?*\" then these numbers are approximately right. If we were playing a different game, we would pass in a different probability list `P` to `win_probability`.\n",
    "\n",
    "Now we can \"solve\" draw poker. In our simplified variant players are allowed to replace one card only. (It is possible that `nocard` is the best action, e.g. if you already have a full house, straight, or flush.) `draw_best_action` returns the action that maximizes the expected value of the resulting hand. That is, it finds the card that, when discarded, maximizes the expected (mean) win probability over all possible replacement cards. It is traditional to use `E` for expected value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_best_action(hand, P=deal_win_P) -> Card:\n",
    "    \"\"\"What card should you discard (or None) from this hand?\"\"\"\n",
    "    def E(c): return mean(win_probability(h, P) for h in discard_outcomes(c, hand))\n",
    "    return max([nocard, *hand], key=E)\n",
    "\n",
    "def discard_outcomes(card, hand) -> list:\n",
    "    \"\"\"All the hands that result from replacing `card` in `hand` with a new card from the `deck`.\"\"\"\n",
    "    return ([hand] if not card else\n",
    "            [replace(card, new, hand)\n",
    "             for new in deck if new not in hand])\n",
    "\n",
    "def replace(old, new, alist) -> list:\n",
    "    \"\"\"Replace old with new in alist.\"\"\"\n",
    "    return [new if c == old else c for c in alist]\n",
    "\n",
    "nocard = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2d'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_best_action(cards('2d 7d 4d 9d As'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This says that with the given hand the best action is to discard the low card, the 2 of diamonds. Drawing a new card could give us a pair of 4s, 7s, 9s, or aces, or it could leave us with only an ace high. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kc'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_best_action(cards('2d 3d 4d Kc As'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the best action is to discard the King, even though it is a high card, to try for the straight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gathering Statistics for Type Probabilities\n",
    "\n",
    "I would like to gather some hand-type probabilities for different game variants. When we are just dealing five cards, it is easy enough to use combinatorics to compute exactly the proportion of hands of each type, as [Wikipedia's does](https://en.wikipedia.org/wiki/Poker_probability). But with complex games involving drawing from the deck or choosing community cards it can be easier to estimate probabilities by doing a simulation.\n",
    "\n",
    "I'll use the notion of a **handmaker**: a function of zero arguments (except maybe optional parameters) that when called returns a random hand, according to the best possible strategy. So `texas_handmaker` deals seven cards, uses `split` to split them into 2 hole cards and 5 community cards, and then determines the best hand that can be made from those cards with `texas_best`. The other handmaker functions do likewise. The `draw_handmaker` samples 5 cards, decides what the optimal action is, and then draws a random card to replace the discard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deal_handmaker()   -> Hand: return deal(5)\n",
    "def texas_handmaker()  -> Hand: return texas_best(*deal(2, 5))\n",
    "def omaha_handmaker()  -> Hand: return omaha_best(*deal(4, 5))\n",
    "def joker_handmaker()  -> Hand: return joker_best(deal(5, deck=deck54))\n",
    "def threeD_handmaker() -> Hand: return threeD_best(*deal(2, 15))\n",
    "\n",
    "def draw_handmaker(P=deal_win_P) -> Hand:\n",
    "    hand = deal(5)\n",
    "    discard = draw_best_action(hand, P)\n",
    "    return replace(discard, draw_card(deck, butnot=hand), hand)\n",
    "\n",
    "handmakers = (deal_handmaker, draw_handmaker, joker_handmaker, \n",
    "              texas_handmaker, omaha_handmaker, threeD_handmaker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deal(hole, community=0, deck=deck): \n",
    "    cards = random.sample(deck, hole + community)\n",
    "    return cards if not community else (cards[:hole], cards[hole:])\n",
    "\n",
    "def draw_card(deck, butnot=()) -> Card:\n",
    "    \"\"\"Randomly draw a card from deck, but make sure it is not in `butnot`.\"\"\"\n",
    "    while True:\n",
    "        card = random.choice(deck)\n",
    "        if card not in butnot:\n",
    "            return card"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to run a simulation. The function `estimated_type_frequency` runs a handmaker repeatedly and returns a counter of frequencies for hand types. We use a `lru_cache` decorator so that we don't wait a long time if we repeat a calculation in a cell farther down in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "@lru_cache(None)\n",
    "def estimated_type_frequency(handmaker, n) -> Counter:\n",
    "    \"\"\"A counter of hand type names for n hands made by handmaker.\"\"\"\n",
    "    return Counter(type_name(handmaker()) for _ in range(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high card': 508,\n",
       "         'one pair': 421,\n",
       "         'flush': 2,\n",
       "         'two pair': 44,\n",
       "         'full house': 3,\n",
       "         'three of a kind': 18,\n",
       "         'straight': 3,\n",
       "         'four of a kind': 1})"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_type_frequency(deal_handmaker, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats(handmakers, n=10**5):\n",
    "    \"\"\"Print hand type probability statistics for each handmaker.\"\"\"\n",
    "    samples = [estimated_type_frequency(h, n) for h in handmakers]\n",
    "    def name(h): return h.__name__.split('_')[0]\n",
    "    print(f'{\"Type of Hand\":15}  {join(f\"{name(h):>8s}\" for h in handmakers)}')\n",
    "    print(f'{\"-\"*15}  {join(\"-\"*8 for h in handmakers)}')\n",
    "    for t in reversed(type_names):\n",
    "        print(f'{t:15} ', join(f'{s[t]/n:8.4%}' for s in samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of Hand         deal     draw    joker    texas    omaha   threeD\n",
      "---------------  -------- -------- -------- -------- -------- --------\n",
      "five of a kind    0.0000%  0.0000%  0.0070%  0.0000%  0.0000%  0.0000%\n",
      "straight flush    0.0000%  0.0050%  0.0190%  0.0410%  0.0990%  0.1790%\n",
      "four of a kind    0.0300%  0.0600%  0.2840%  0.1930%  0.4540%  1.5450%\n",
      "full house        0.1490%  0.6540%  0.3300%  2.5750%  6.3800%  9.7730%\n",
      "flush             0.1970%  0.7830%  0.3230%  2.9890%  6.7390%  7.5840%\n",
      "straight          0.3980%  1.2720%  1.0650%  4.5740% 11.2920% 18.6330%\n",
      "three of a kind   2.1880%  3.7060%  7.4280%  4.8560%  8.8300% 25.9350%\n",
      "two pair          4.7220%  9.6350%  3.8690% 23.6260% 36.7720% 28.3140%\n",
      "one pair         42.1940% 48.0430% 45.5100% 43.8630% 26.4810%  8.0260%\n",
      "high card        50.1220% 35.8420% 41.1650% 17.2830%  2.9530%  0.0110%\n",
      "CPU times: user 4min 25s, sys: 161 ms, total: 4min 25s\n",
      "Wall time: 4min 25s\n"
     ]
    }
   ],
   "source": [
    "%time stats(handmakers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `deal` column shows good agreement with the [known poker probabilities](https://en.wikipedia.org/wiki/Poker_probability). The other columns show that hands get better as we allow more options in the game, with Omaha yielding much higher hands than Texas, but Three-D yielding the highest hands of all. There is an inversion in Omaha where straights are more probable than three of a kind. It appears that is because many of the three-of-a-kinds become full houses (which are just three-of-a-kinds where the other two cards happen to match). In Three-D, there is an inversion in that full houses are more probable than flushes; perhaps a flush should beat a full house in that game.\n",
    "\n",
    "# Draw Poker Revisited\n",
    "\n",
    "Now that we have seen the stats for what types of hands we can achieve in draw poker by following `draw_best_action`, it makes sense that we modify the best actions! That is, `draw_best_action` assumed that \"high card\" wins about 50% of the time. But we see in the `draw` column of the stats chart that if players use their draw card properly, a high card will only win about 35% of hands. That should change our actions, making us more willing to take risks to get a pair or higher. \n",
    "\n",
    "The new cumulative winning percentages for draw poker, assuming players will chose the best action for their draw, can be computed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.35842,\n",
       " 0.83885,\n",
       " 0.9352,\n",
       " 0.97226,\n",
       " 0.98498,\n",
       " 0.99281,\n",
       " 0.99935,\n",
       " 0.99995,\n",
       " 1.0,\n",
       " 1.0]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = estimated_type_frequency(draw_handmaker, 10**5)\n",
    "draw_win_P = cumsum([f[type_names[type]] for type in types])\n",
    "draw_win_P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's consider the following hand under both sets of probabilities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4s'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hand = cards('4s Qc 6d 7s 8c')\n",
    "\n",
    "draw_best_action(hand, P=deal_win_P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the  old `deal_win_P` probabilities, the best play is the safe play: discard the low card. We'll end up with either a pair or at least a queen high.\n",
    "\n",
    "But under the new `draw_win_P` probabilities, the best action has changed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Qc'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_best_action(hand, P=draw_win_P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a risky play: we give up our high card, so our hand could get worse. But there's a 4/47 chance of greatly improving the hand by hitting the inside straight; the same 16/47 chance of getting a pair of some kind (but not a pair of queens), and a 11/47 chance of getting a high card of queen or better. The difference in actions is because under `draw_win_P`, a queen high is much less likely to win, so it is worth sacrificing the queen for a chance at a straight.\n",
    "\n",
    "Now I will define a new handmaker that uses these probabilities, and I will regenerate the stats chart (note that the `@lru_cache` dedcorator on `estimated_type_frequency` means that I won't have to wait another five minutes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of Hand        draw2     deal     draw    joker    texas    omaha   threeD\n",
      "---------------  -------- -------- -------- -------- -------- -------- --------\n",
      "five of a kind    0.0000%  0.0000%  0.0000%  0.0070%  0.0000%  0.0000%  0.0000%\n",
      "straight flush    0.0050%  0.0000%  0.0050%  0.0190%  0.0410%  0.0990%  0.1790%\n",
      "four of a kind    0.0770%  0.0300%  0.0600%  0.2840%  0.1930%  0.4540%  1.5450%\n",
      "full house        0.7080%  0.1490%  0.6540%  0.3300%  2.5750%  6.3800%  9.7730%\n",
      "flush             0.7820%  0.1970%  0.7830%  0.3230%  2.9890%  6.7390%  7.5840%\n",
      "straight          1.3670%  0.3980%  1.2720%  1.0650%  4.5740% 11.2920% 18.6330%\n",
      "three of a kind   3.6240%  2.1880%  3.7060%  7.4280%  4.8560%  8.8300% 25.9350%\n",
      "two pair          9.5970%  4.7220%  9.6350%  3.8690% 23.6260% 36.7720% 28.3140%\n",
      "one pair         47.9090% 42.1940% 48.0430% 45.5100% 43.8630% 26.4810%  8.0260%\n",
      "high card        35.9310% 50.1220% 35.8420% 41.1650% 17.2830%  2.9530%  0.0110%\n"
     ]
    }
   ],
   "source": [
    "def draw2_handmaker() -> Hand: return draw_handmaker(P=draw_win_P)\n",
    "\n",
    "stats((draw2_handmaker, *handmakers))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be truly accurate, we should repeat this process of using updated probabilities until we hit a fixpoint. But we can see that there is not that much difference between the `draw` and the `draw2` columns, so I'm not going to bother with that.\n",
    "\n",
    "Below I compare the two win probabilities for the test hands, `deal_win_P` on the left and `draw_win_P` on the right. We can see that a three-of-a-kind or better is very likely to win under either condition, but that a pair of 2s, which has a 53% chance of winning on the deal, only has a 39% chance after the draw.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As Ac Ah Ad Ad  100.00000%  100.00000%  five of a kind\n",
      "Kh Kd Ks Kc Kh  100.00000%  100.00000%  five of a kind\n",
      "3h 3s 3d 3c 3c  100.00000%  100.00000%  five of a kind\n",
      "2s 2c 2d 2h 2h  100.00000%  100.00000%  five of a kind\n",
      "As Ks Qs Ts Js  100.00000%  100.00000%  straight flush\n",
      "Kc Qc Jc Tc 9c   99.99988%   99.99962%  straight flush\n",
      "6d 5d 4d 3d 2d   99.99905%   99.99692%  straight flush\n",
      "5h 4h 3h 2h Ah   99.99893%   99.99654%  straight flush\n",
      "As Ac Ad Ah 2s   99.99846%   99.99500%  four of a kind\n",
      "7s 7c 7d 2d 7h   99.98553%   99.96269%  four of a kind\n",
      "6s 6c 6d 6h 9s   99.98369%   99.95808%  four of a kind\n",
      "As 5h 5c 5d 5s   99.98184%   99.95346%  four of a kind\n",
      "Th Tc Td 5h 5c   99.93013%   99.73377%  full house\n",
      "9h 9c 9d 8c 8h   99.91904%   99.68346%  full house\n",
      "6h 6c 6d Tc Th   99.88580%   99.53254%  full house\n",
      "5c 5d 5s As Ah   99.87472%   99.48223%  full house\n",
      "As 2s 3s 4s 6s   99.83039%   99.28100%  flush\n",
      "Kc Qc Jc Tc 2c   99.81528%   99.22077%  flush\n",
      "Qc Jc Tc 9c 7c   99.80016%   99.16054%  flush\n",
      "4h 5h 6h 7h 9h   99.75480%   98.97985%  flush\n",
      "As Kd Qc Td Jh   99.63385%   98.49800%  straight\n",
      "Kc Qh Jd Th 9c   99.60366%   98.40015%  straight\n",
      "6c 5d 4h 3s 2s   99.39234%   97.71523%  straight\n",
      "As 2d 3c 4h 5s   99.36215%   97.61738%  straight\n",
      "As Ac Ad 2h 3h   99.24139%   97.22600%  three of a kind\n",
      "Ts Tc Th 9s 8c   98.59128%   96.08569%  three of a kind\n",
      "Ts Tc Th 9s 7c   98.59128%   96.08569%  three of a kind\n",
      "9h 9s 9d Ah Kh   98.42876%   95.80062%  three of a kind\n",
      "Ts Tc 5s 5c 8h   95.66580%   90.55538%  two pair\n",
      "Ts Tc 5s 5c 7h   95.66580%   90.55538%  two pair\n",
      "9s 9c 8s 8c As   95.30012%   89.81423%  two pair\n",
      "3s 3c 2s 2d Ah   93.10601%   85.36731%  two pair\n",
      "As Ac 4c 5s 6s   92.37464%   83.88500%  one pair\n",
      "4s 4c As Ks Qs   59.86933%   46.92885%  one pair\n",
      "4h 4d Kh Qd Jd   59.86933%   46.92885%  one pair\n",
      "2d 2c Ad Kd Qd   53.36827%   39.53762%  one pair\n",
      "Ah 3s 4s 5s 6s   50.11774%   35.84200%  high card\n",
      "Kh Qh Jh Th 8d   46.26253%   33.08492%  high card\n",
      "7d 2s 4s 5s 6s   23.13126%   16.54246%  high card\n",
      "7h 6s 5d 3s 2d   23.13126%   16.54246%  high card\n"
     ]
    }
   ],
   "source": [
    "for h in hands:\n",
    "    p1, p2 = win_probability(h, P=deal_win_P), win_probability(h, P=draw_win_P)\n",
    "    print(f'{join(h)}  {p1:10.5%}  {p2:10.5%}  {type_name(h)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "\n",
    "# What Comes First?\n",
    "\n",
    "Bram Cohen suggests an interesting experiment: shuffle a deck and flip over cards one at a time, until either a flush or a straight appears among the face-up cards. Among five-card hands, a straight is more likely, so shouldn't it come up first more often? Let's create a simulation to see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flush_or_straight_first(deck=deck) -> str:\n",
    "    \"\"\"Deal cards randomly until a straight or flush show up among any of the cards.\"\"\"\n",
    "    random.shuffle(deck)\n",
    "    suits   = Counter() # Count how many we have of each suit\n",
    "    rankset = set()     # Set of all the ranks of the cards so far\n",
    "    first   = ''        # What came first?\n",
    "    for card in deck:\n",
    "        suits[suit(card)] += 1\n",
    "        rankset.add(rank(card))\n",
    "        if is_straight(rankset): first += 'straight'\n",
    "        if 5 in suits.values():  first += 'flush'\n",
    "        if first:                \n",
    "            return first\n",
    "        \n",
    "def is_straight(rankset) -> bool:\n",
    "    \"\"\"See if any possible straight is a subset of the rankset.\"\"\"\n",
    "    return any(straight.issubset(rankset) for straight in straights)\n",
    "\n",
    "straights = [set(range(start, start + 5)) for start in range(2, 11)] + [{14, 2, 3, 4, 5}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'flush'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flush_or_straight_first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'flush'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flush_or_straight_first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'flush': 5191, 'straight': 3809, 'straightflush': 1000})"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(flush_or_straight_first() for _ in range(10**4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that a straight is a more probable hand than a flush in general, but\n",
    "counterintuitively, a flush is more likely to come up first, by about 50% to 40%, with about 10% of the time both of them coming up on the same card (a straight flush). One explanation for this is the [pigeonhole principle](https://en.wikipedia.org/wiki/Pigeonhole_principle): there are only four suits (pigeonholes), each of which can only fit four cards (pigeons) before we get a flush. So that means, in the worst case, the first 16 cards fill all the holes, and the 17th card **must** make a flush. But for a straight, it is possible to go up to 44 cards  without making any straight (every straight must contain either a 5 or a 10; so you could deal the other 11 ranks in all four suits and not have a straight).\n",
    " \n",
    "We can extend Bram's experiment to all the  types. The function `what_first` simulates a single pass through the deck, flipping up cards until every type has appeared, and returning as a result a dict of `{type_number: number_of_cards_to_make_that_type}`. We'll leave out type 0, \"high card\", because the very first card always makes a high card."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def what_first(deck=deck) -> list:\n",
    "    \"\"\"Flip cards; record how many cards it took for each type.\"\"\"\n",
    "    random.shuffle(deck)\n",
    "    suits    = Counter()   # Count how many we have of each suit\n",
    "    ranks    = Counter()   # Count how many we have of each rank\n",
    "    rankset  = set()       # Set of all the ranks of the cards so far\n",
    "    ranksets = {s: set() for s in 'shdc'} # As above, but per suit\n",
    "    numbers  = {}          # The number of cards required to make each type\n",
    "    kind     = lambda n: sum(ranks[r] >= n for r in ranks)\n",
    "    def record(type, i): \n",
    "        if type not in numbers: numbers[type] = i\n",
    "    for i, card in enumerate(deck, 1):\n",
    "        suits[suit(card)]    += 1\n",
    "        ranks[rank(card)]    += 1\n",
    "        rankset              |= {rank(card)}\n",
    "        ranksets[suit(card)] |= {rank(card)}\n",
    "        if 8 not in numbers and any(is_straight(ranksets[suit]) for suit in 'sdhc'): \n",
    "                                     record(8, i) # straight flush\n",
    "        if kind(4):                  record(7, i) # four of a kind  \n",
    "        if kind(3) and kind(2) >= 2: record(6, i) # full house\n",
    "        if 5 in suits.values():      record(5, i) # flush\n",
    "        if 4 not in numbers and is_straight(rankset): \n",
    "                                     record(4, i) # straight\n",
    "        if kind(3):                  record(3, i) # three of a kind\n",
    "        if kind(2) == 2:             record(2, i) # two pair\n",
    "        if kind(2):                  record(1, i) # one pair\n",
    "        if len(numbers) == 8:\n",
    "            return numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A sample run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 7, 2: 9, 6: 11, 3: 11, 4: 12, 5: 15, 7: 23, 8: 30}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "what_first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will define `what_firsts` to run `n` simulations and return a dict keyed by type number, where each value is a Counter of the card numbers where the type first appeared across all the simulations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def what_firsts(n) -> dict:\n",
    "    \"\"\"Run what_first() n times and record numbers of cards as Counters:\n",
    "    returns dict of {type_number: Counter(how_many_cards: how_often})}\"\"\"\n",
    "    counters = {type: Counter() for type in types}\n",
    "    for trial in range(n):\n",
    "        numbers = what_first()\n",
    "        for type in numbers:\n",
    "            counters[type][numbers[type]] += 1\n",
    "    return counters        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: Counter(),\n",
       " 1: Counter({5: 1, 8: 1, 10: 1, 9: 2, 6: 2, 2: 1, 7: 1, 3: 1}),\n",
       " 2: Counter({10: 4, 11: 2, 7: 1, 8: 2, 4: 1}),\n",
       " 3: Counter({13: 1, 15: 3, 11: 1, 14: 3, 17: 1, 9: 1}),\n",
       " 4: Counter({9: 2, 14: 1, 17: 2, 13: 1, 19: 1, 10: 2, 16: 1}),\n",
       " 5: Counter({12: 2, 9: 1, 8: 1, 13: 1, 10: 1, 11: 3, 16: 1}),\n",
       " 6: Counter({13: 1, 15: 3, 11: 1, 14: 3, 17: 1, 9: 1}),\n",
       " 7: Counter({31: 2, 25: 1, 34: 1, 12: 1, 21: 1, 20: 1, 19: 1, 26: 1, 27: 1}),\n",
       " 8: Counter({22: 1, 14: 1, 20: 2, 36: 1, 28: 1, 33: 1, 23: 1, 25: 1, 34: 1}),\n",
       " 9: Counter()}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "what_firsts(10) # Run 10 simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that type 1 (one pair) has a Counter with mostly low numbers&mdash;a pair tends to show up early. Type 8 (straight flush) has a Counter with mostly high numbers; it takes a lot of cards before you are likely to get a straight flush. \n",
    "\n",
    "Let's run 100,000 simulations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36.2 s, sys: 12.8 ms, total: 36.3 s\n",
      "Wall time: 36.3 s\n"
     ]
    }
   ],
   "source": [
    "%time counters = what_firsts(10**5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw numbers are messy to look at, so I'll plot them, showing for each type the distribution of how many cards it took, and in the legend showing the mean number of cards for each type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_what_firsts(counters, types=range(1, 9), \n",
    "                     colors='w b g r c m y k orange'.split()):\n",
    "    N = sum(counters[min(types)].values())           # How many trials\n",
    "    M = max(L for c in counters.values() for L in c) # Longest trial\n",
    "    X = range(1, M + 1)                              # Numbers to plot\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.xlabel('Number of cards needed')\n",
    "    plt.ylabel(f'Percent (out of {N:,d} trials)')\n",
    "    plt.grid(True, which='major'); \n",
    "    plt.grid(True, which='minor', linestyle=':', alpha=0.5, markevery=1)\n",
    "    plt.minorticks_on()\n",
    "    for type in reversed(types):\n",
    "        Y = [100 * counters[type][x]/ N for x in X]\n",
    "        μ = sum(L * n for L, n in counters[type].items()) / N\n",
    "        plt.plot(X, Y, 'o:', lw=2, alpha=0.8, color=colors[type],\n",
    "                 label=f'{type}: μ={μ:4.1f} {type_names[type]}')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_what_firsts(counters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, that looks kind of like a side view of the rollercoasters at Six Flags amusement park. Let's see if we can make sense of it. There seem to be three distinct patterns. First, there are three hand types with an early sharp peak: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_what_firsts(counters, types=(1, 2, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed, the flush can go no more than 17 cards. Also by the pigeonhole principle, the pair can go no more than 14 cards (one card in each of the 13 ranks, and then the 14th card must form a pair) and two pair can go no more than 17 cards (one card in each of the 13 ranks, then 3 more cards in one of the ranks (making four of a kind), and then the 17th card must make a second pair).\n",
    "\n",
    "Next are three tightly-grouped Gaussian-looking curves. It makes sense that three of a kind and full house are close together, because usually when you get three of a kind, you have gotten some other pair previously. I'm not sure why straight is so close to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i+07b0crHEleioNc86rXfq+Ekc6/P3AcXtCeqvJYcLTT02l5aaMQ7roQNvCRJelGSpAZJkr7p8N5wSZL+KUnSgfbf0VclawSlCwa15qjRUAMt4tKKo1bjkMvFtRUV3Hv4MP4oax/DxVXb1oZXCOrCLMTUu3e9cfTa79VwkrnXZ+7jdVajHjhaaOi1vbTQ+Dad1fgy0HUL4GXAv4QQ44F/tb/WBdra2nTJUaOhBlrEpRVHKdZarcwrL2fCjh3MKy/n8+ZmPLJMi99Pa5S/cMLFdXFODi9MmMCyMWO6nVivN++90RjI/V4NJ5l7feZeluWElteSo4WGXttLC414x5WwgZcQ4lOgscvb3wdeaf/3K8AlidKPFVrd99XiPrka6PX+faL8dzz+Z7jRSG1bGyuPHOHq3FyeOeUUsqLoRopryqBBGNoHXR5Z5tX6evxC6Mp7bzUGcr9Xw0nmXp+57/rHUbzLa8nRQkOv7aWFRrzjSuh2EpIkFQF/F0KUtL9uFkIM7fB5kxAi7O1GSZJ+AvwEYOTIkdNff/31mLRbWloYNGiQ4vKyLMc8nagFR41GrN61iksLjlLvdxL4q8ANpAhBpiThBoYDj8UprheAdbgQOBFCJk+y8SP8zGSiAgVtcq/XPOq136vhaNXGAzH3Q4YMYdy4cfj9fowxbucihIjpP9Seyvv9fs4++2zy8/NZs2aNao1onLfeeouHH36Yffv2sX79eqZNm4bf72fnzp38/Oc/D/HvvfdeFi5c2I1/++23s3PnToQQjBs3jqefflpRnzly5Ahbtmzhhz/8Ycw+zj33XD7++OMey5aUlPDJJ58wfPjwTt43btyI2WwOnQwQDrHmPt45ATh48GC3tYlz586NuJ1E2JOz4/UDFAHfdHjd3OXzJiX1TJ8+vceTwcOh42nvStDc3ByzhhYcNRqxelejo9f2Uuq9aPNmUbxli8j45BOR+cknonTrVjHtyy/F2M2b4xbX/6v4UKSu/0CMXr9aTFj/ijhp/V/EqPWrxeuHP4ibRlck+31s0KuXZO6VYc+ePUIIIex2u6Ly71ssYu7OnaJo82YxZ8cO8b7FoljL6/VG/Ox3v/uduPLKK8WCBQsUc9To7NmzR1RUVIizzz5bbN26VQgR8O50OkO8mpoaMWLEiLD12Gy2kMadd94pHn74YUUxrV+/Xlx00UUxxRuL95NOOkmcOHGiG+eBBx4Qq1at6pGrNPdq4lLKCfbDjgC2iQhjGq2faqyXJCkfoP23+mPo44zUMFsA6IGjRkMNtIhLK44SjE1LwycEI81msk0mjCg//kdpXGsajjGSEwzChmSADIOPFPz87riynbm1yL1e86jXfq+Go6d+31sNvbaXEnRdXlDn8bDkwAHWWq2K+JFm4Y4dO8Y//vEPbrrpJkWclStX8uijj4Zel5SUUFVVFVUHYNKkSWGPAsrIyAjdonW73RFnZwYPHgwEbp25XK6w5T755JPQGZGnnXYaDoeDZcuW8dlnn1FaWspjjz3Gyy+/zOWXX87ChQs5//zzaWlp4ZxzzmHatGlMnjyZd955J+QjOKMmyzK33XYbxcXFXHzxxVx00UW88cYbId0//elPnH766UyePJmKigqqqqp45plneOyxxygtLQ179qQa9PfF9eHwLnBd+7+vA97RWD8iop3v1FccNRpqoEVcWnGUYGlhIV4hSJUkhhoMOP1+PEKwtLAwbnEd92eRRmChsBAybpGCwMAxn7KntrTIvV7zqNd+r4ajp37fWw29tlcQZdu2UdbllJM7Dx6kbNs27q+sxCxJZBqN2Px+jra1Yff5WFVdDcAJj4eybdu4cNeusHVHWmD9i1/8gt/+9rfd/nNesWIF77wT239xs2fP5rTTTut2SHa0W3UAW7Zsobi4mMmTJ/PMM89EXCt3ww03MGrUKCoqKrjjjju6ff7oo4/y5JNPUl5ezsaNG0lPT+eRRx7hrLPOory8nDvvvBOAzZs388orr7Bu3TrS0tJ466232LFjB+vXr+dXv/pVty0Y/va3v1FVVcXXX3/N888/z+bNmzt9npOTw5dffsmtt97Ko48+SlFRET/96U+58847KS8vZ/bs2UqbsUf068X1kiS9BmwGJkiSdEySpBuBR4DzJEk6AJzX/loXGDJkiC45ajTUQIu4tOIowfzsbJ4YP5781FTsQpCfmsoT48czX+HmjeHislrfZ8+eq/F4AhO5BaYW2khHMqTiIY1aMYx6hpDDCSyW91RpxBt6zaNe+70ajp76fW819NpeSlDr8ZDRZXCUIklUud2K+OHWEf39738nNzeX6dOnd/vswQcf5JJLYnt+bOPGjd0OyC4vL+fcc8+Nyp05cya7d+9m69atPPzww7gj+HrppZc4fvw4kyZNYvXq1d0+P/PMM/nlL3/J448/TnNzc2gA13V27LzzzmP48OFAYMnSfffdx5QpUzj33HM5fvw4FoulU/nPPvuMyy+/HIPBQF5eHnPnzu30+aWXXorRaGT69OmdZgDjjVjXAqrl9IREPtV4pRAiXwiRIoQoEEK8IISwCiHOEUKMb//d9anHPoNV4XSz1hw1GmqgRVxacaJBFoI7Dx7EJct8NGUKX5x8MutKSxUNuqzWtZSXz2PLlpMpL5+H1bo29JnNtpHW1n1YLG8B8KvRo/BiwiWnkCI8pOAlFQ/XGP+F329XoJX43Os1j3rt92o4eun38dDQa3sFsa2sjG1lndczPzZuHNvKypiUkUFr+8zFUJOJienpDDGZQssLRpjNbCsr44OpU8PWHe7w5U2bNvHuu+9SVFTEj370I9atW8ePf/zjHjlApw2WOx5HM3v27G6zXUpnvIKYNGkSmZmZfPPNNxHLCCG44oorePPNN7t9tmzZMp5//nlcLhezZs2ioqKiW8zQeb+1V199lRMnTrB9+3bKy8sZOXIkLS0tET2HQ2pqKj6fD6PRGPWg695ATd3xjie5c307Ro4cqUuOGg010CIurTjR8LnNxsbmZl6uq8MgSYo1rNa1HDiwhLa2WlJShuJ07mbfvltCg6+8vBsoKlpJXt5iAK4YewG/P2kIuSY3DtKZYDzBsyelcsvUe8nNvSqqnha512se9drv1XD00u/joaHX9lKCpYWFeITA6fcjhKBVlmNaXpDS5dgwgIcffphjx45RVVXF66+/zrx58/i///u/HjkAW7duBQJPC1ZVVYVuy23cuJFdu3bFPONVWVkZGhwcOXKEffv2UVRU1KmMEIKDBw8CgS073nvvPSZO7P6E9aFDh5g8eTL33HMPZWVlVFRUkJWV1W0g1RE2m43c3FxSUlJYv349R44c6eb9rLPO4s0330SWZerr69mwYUO3erpysrKycDgcPXqPFZFyEm9OT0gOvNpRU6NswbPWHDUaaqBFXFpxomHm4ME8NHYsS0aPxiBJijWqq1chSWaMxkw8Hgt+fwuy3Ep19SoAMjJOISfnYgyGf39Jrxh7AV+edQPbTjmTrbNv4IqxFzB48Ay8QlDT1obPZ2ffvltwOvd009Mi93rNo177vRqOXvp9PDT02l5K0HF5QZPfz8iUlJiWF3jCnEjRE1asWBF2RgmgqamJGTNmsHjxYi699FLuueceRTpvvfUWBQUFbN68mQULFnDBBRcAgdt4U6dOpbS0lEWLFvHUU0+R037U2UUXXURNTQ1CCK677jomT55MSUkJtbW1rFixopvGH/7wB0pKSpg6dSrp6enMnz+fKVOmYDAYmDp1Ko891n3Dnauvvppt27ZRVlbGq6++ysSJE7v5+MEPfkBBQQElJSXccsstzJw5s9tt5a6chQsX8tZbb8V1cX2seVTL6QkJ3ccrXigrKxPbuiyYjIYNGzYwZ86cxASkcyS9z0lI3V98MRajMbDPjBBevN4GjMbhCOFi1qzDiuupa2vjFwcP4hOCRwavp7nhFQYNmsKECS/0eqO+ZO7n9HUYfYaB6H/v3r1MmjQJh8NBVlZWX4ejCCtXrmTQoEHcddddcanv2+Q9uNec1Wrl9NNPZ9OmTeTl5fWqTj34D/bDjpAkKeI+XskZr3YMtL/+equj1/aKhnB/aCjVSEnJQZZbAfD5wGweDcikpRVF5XbUGGoy4QdkICXnekaO/DFjx/53t0FXctYj8dCrl2TuE49YZzG0mimJ9+xKvDTi0V4XX3wxpaWlzJ49m+XLl3cbdPUX79GgzdkM3wKMGjVKlxw1GmqgRVxacSLB4fNxbUUFFw0fzo35+aGjfZRoNDZ+jMfTjM9nwWzOw2TKwO93IoSHwsKlUfkdNdKMRv4wbhzDTCYyjEbI+EWnsocPL8dm20Bb2zEaGsZSWLiU7Oz5MbpVBr3mUa/9Xg2nr/t9PDX02l5qYDabE1o+EmflypVx14kV8fISa/lw67p6o6EGWniPhuSMVzvq6up0yVGjoQZaxKUVJxL+1dREtdvNzpaW0KBLqYbRmEFa2ijy828mNTUfj+cEqan5jB//hKJBUVeN0ampgUFXO+T2mbiqqv+muvq3OBw7keVBtLXVcuDAkk5PT8YTes2jXvu9Gk5f9/t4aui1vdSg49OEiSivJUcLDb22lxYa8Y4rOePVjtzcXF1y1GiogRZxacWJhO/n5DA6NZW0Lnv5KNEYMuS7FBe/QUpKDpIkxXz+XCQNWQherqtjh8PB4+PH09j4PgZDOikp2ZhMqUAqfn9gYX8iZr30mke99ns1nL7u9/HU0Gt7qYFeDzvvLwekD2Tv0ZCc8WpHY2PsW4ppwVGjoQZaxKUVJxIkSWLG4MFM7nIobCQNWfbR1nY89NpsHhFahxWv9rL5fLze0MAXdjvbHQ48nuOkpZ2MyTQs9Hi4wZCB210Vk15v4+prjl77vRpOX/f7eGrotb3UINa9mbTa/ymRe1j1RkOv7aWFRnIfrwQheIaV3jhqNNRAi7i04oSDu8vxFUo0jh37PXv2XIXNtrnbZ/Fqr2EpKfxm7FieGD+eGYMHk5Y2Fll2AcHdkv34/Q5FC/jVQK951Gu/V8Ppy34fbw29tpcaxLobuVY7nsd7l/R4aei1vbTQ+NbsXB8PSJK0UJKkZxsbG2ltbcXhcGC323G5XDQ2NuL1emloaEAIQW1tLfDvJ2K8Xi9CCBoaGvB6vTQ2NuJyubDb7TgcDlpbW2lubsbj8WCxWGhpaQmtLQjWEfxdX1+Pz+fDarXidrux2Ww4nU6sVis2mw23243VasXn81FfXx+2jrq6OmRZpqamBo/HQ3NzsyJPra2toTpqa2sVeZJlGYvFgizLij01NDTgdDpxOp2KPLW2toY8WSwWRZ6cTme3PEXz1NjY2ClP0TwJIbrl6RurlXk7dvC7ysqwnjq2cdDTiRP1uN0NeL0uvF5jN092uz1i3wvnqampKWLfmzFoEEWuwGArI2MxQnhoa7Ph9dpwOivwei0MGXJLJ09K89TRU7g8nThxQtH3qaMnu90e9fvUNU8d2zjS96mjp9ra2pg9ybIc0zWitrYWp9Op+BoR9NTRixJPDQ0NMefJZrMpvkbE8n3q6Kmmpiama4TNZsNqtcbc91paWhRfI4QQHD16VLEnWZbxt2+IKsty6LofXJMTfBot+Dv4uc/nw+/3h34H/y3LMj6fT1EdXq+X1tZWZsyYwdSpUykuLmb58uWhOjrG07UOj8cTqqNj+Y4xda3D6/WyZs0aTj31VAwGQ+isQyEEW7ZsCe3jNWXKFN58882wnjweD/fffz8TJkxg0qRJ/P73v+/maceOHbz33nuhdlmxYgWrVq0KxarUU/B3sJ5InqLlqb6+ntNPP53TTjuNDRs2dPMUfFI9Uh3XXnstf/3rX7vl/MYbb+Srr76KydOgQYPCehJCdLvu9Yhg4Hr+mT59uogV69evj6m80+mMWUMLjhqNWL2r0dFre4Xz/mpdnZi+dav476qqmDRkWRZO5764xKW0fJXLJR7f/6HYuXOu2LSpQHz6aZbYtWuhkGW/In6y38cGvXrRwn9/yP2ePXuEEELY7XZlhPffF2LuXCGKioR/zpzAa4Xw+Xzd3pNlWTgcDiGEEB6PR5x++uli8+bNPXLU6ASxZ88eUVFRIc4++2yxdetWIUTAu9PpFF6vVwghRE1NjRgxYkTodQ0aOkoAACAASURBVEe8+OKL4pprrhEej0cIIUR9fX23Mi+99JK4/fbbQ68feOABsWrVqh7jkmVZ+P2dr1Hx8v7aa6+Ja6+9NiInWu6vu+46sWbNmrjElZmZGfbzYD/sCGCbiDCm0fWMl5boepK6XjhqNNRAi7i04nTFVSNHsrq4mOsjbNTXUcPnsyNE4Dw3SZLIyDglLnEpKd/q97O4ooLHLVnc4PstV7Ga5ZkfcXz0S0hSYr6qes2jXvu9Gk5f9ftEaOi1vRRh7VpYsgRqa2H4cKS6usDrteqfGJYkiUHta0a9Xi9erzfqJsgrV67k0UcfDb0uKSlRfCj0pEmTmDBhQrf3MzIyQgvA3W53xBiefvppVqxYEXowqOuDDB6PhxUrVrB69WpKS0tDh2jv2bOHefPmcfLJJ/P4448DUFVVxaRJk7jtttuYNm0a1dXVfPTRR5xxxhlMmzaNK664InTM0Pbt2zn77LOZPn06F1xwQWimtiOOHDnCeeedx5QpUzjnnHM4evQo5eXl3H333bz//vuUlpbiar8zEMSDDz7I2WefTUlJCT/5yU+inge5fPlyFi9ejCzLzJkzh+DG7IMGDeL+++9n6tSpzJo1KzSDW1lZyRlnnMGsWbNYvnx5j3XHguTAqx3REtZXHDUaaqBFXFpxwuE76enkp6b2qCHLbRw4cDuHDv0Kny/yuWRq4lJSPsNo5PSsLCw+Hy6/n6FGI/VeiSUHDrA2QYcm6zWPeu33ajh92e/jraHX9gqhrCzw0xF33hl47/77wWyGzEyw2eDoUbDbYVXgyC9OnAiUu/DCmCT9fj+lpaXk5uZy3nnnMXPmTCBwZNB7770XU12zZ89m+vTpqg7J3rJlC8XFxUyePJlnnnkm7JN4hw4dYvXq1cycOZP58+dz4MCBTp+bzWYefPBBrrjiCsrLy7niiisAqKioYO3atXz55Zf853/+Z+gW4b59+7j22mvZuXMnmZmZPPTQQ3z88cfs2LGD6dOn8/vf/x6v18sdd9zBG2+8wfbt21m8eDH3339/t9iWLFnCNddcw1dffcXVV1/Nz372M0pLSzvFk56e3o3zySef8M033+Byufj73/8esX3uvvtuGhoaeOGFF7o9ke50Opk1axa7du3ie9/7Hs899xwAP//5z7n11lv54osver3Dfkckt5Noh1YHZ8bKiffhnPHS0Wt7dYQsBCe8XkZG2fwuqOF2H6Gt7Tg+nx3o+a/uRLXXFoeDPLOZTKMRWQgyJQn8fv7fkUNM833IyJHRD9dORFxac/Ta79Vw9HAob7w09NpeilBbC103Z01JAYWzTZFmkYxGI+Xl5TQ3N7No0SK++eYbSkpKePDBB5FlOaYQN27cGPNWNUHMnDmT3bt3s3fvXq677jrmz59PWlpapzJtbW2kpaXx5Zdf8vbbb7N48WJFZyAuWLCAtLQ0MjIyyM3NDc0InXTSScyaNQuAL774gj179nDmmWcCgdmzM844g3379vHNN99w3nnnAYGBan5+fjeNzZs388YbbwBwzTXXcPfdd0eNa/369Tz88MO0tbXR2NhIcXExCxcu7FbuN7/5DTNnzuTZZ58NmxOz2czFF18MwPTp0/nnP/8JwKZNm3jzzTeRJIlrrrmm05mavUFy4NUOl8vVbTStB44aDTXQIi6tOEF8ZrNx16FD/Cg3l18WFkbVyMg4hYkT/4wQHkymIRHLq4lLaflKt5vh7U/QyLKMwWgkwyBxoKWG6urfk5Exgays6Yp14xWX1hy99ns1HK37fSI19NpeIYQ70zd4qPO8eYHBV2YmDB0KQ4ZAaysEBwEjRoTntyPagGjo0KHMmTOHDz74gJKSkh45HWf1Om7OOXv2bOx2e7dB3qOPPsq5554bUbsjJk2aRGZmJt988w1lXWb/CgoK+MEPfoAsyyxatIgbbrhBUZ2pqakhL0ajMbS9QmZmZidP5513Hq+99hoQ2ILBZDLx9ddfU1xcHHoYoCd0HBRFu2Xrdru57bbb2LBhA5MmTWLlypW43e6wZWfMmMH27dtpbGxk8ODB3XKSkpIS0uvoLxhHrAPoaEjeamyHmkM2teBodfinFnFpxQmi0u1GAnIj/AVtta6lvHwe+/ZNo7x8HlbrWtLSCkhPPznucSktPzYtjVZZplWWOeb10uD10ioLilLNDB9+Hikp8d1cUq951Gu/V8PRut8nUkOv7aUIS5eCxwNOJwgRGHR5PIH3FSDcAOrEiRM0NzcDgQHjxx9/zMSJE3vkAGzduhUIrGuqqqoKrWvbuHEjO3fupLy8vNNPtEFXZWVlaLBw5MgR9u3bR1FRUbdyl1xyCevWrcNgMPDJJ59wyind17BmZWXhcDgU+e+IWbNmsWnTJg4ePAgEBkb79+9nwoQJnDhxIjTw8nq97N69uxv/u9/9Ln/9618BePXVVznrrLN61AsOsrKzs2lpaQnNloXDhRdeyLJly1iwYAFOp7PHejvizDPP5PXXX8dgMPDqq68q5kVDcuDVjqamJl1y1GiogRZxacUJ4rq8PP4+eTKLRozo9pnVupYDB5bgdh/F47Hich2K6WieRLXX0sJCPELg9vtx+nw0+Xx4hOD+75zOySc/TFpa5Jk7NdBrHvXa79VwtO73idTQa3spwvz58MQTgRmupiZEXl7g9XxlJ0KEW/RfW1vL3LlzmTJlCjNmzOC8884L3bJasWIFb7/9dti6mpqamDFjBosXL+bSSy/tdAurp4cL3nrrLQoKCti8eTMLFizgggsuAOCzzz4LbSexaNEinnrqKXJycgC46KKLQltzLFu2jDfffJMpU6Zw77338vzzz3fTmDt3Lnv27Om0uD5aXAAjRozg5Zdf5sorr2TKlCmcccYZVFRUYDabeeONN7jnnntCMX7++efd+I8//jgvv/wyU6ZM4X//93/54x//2KPe0KFDufnmm5k1axaXXHIJM2bM6LH85Zdfzs0338z3v//9bov0I+GPf/wjTz75JKeffjo2m00RRwkkrRax9gZlZWViWw9TwOGwYcMG5syZo7i8ECLq1GZfcNRoxOpdq7i04Cj1Xl4+j7a2Wny+Rvz+FozGLEymYaSm5lNaui7uccVSfuvq1Rh/9zuyjx3DOno0/rvuYkb7ItdoSPb7OQmNSw1HqzYeiLnfu3cvkyZNwuFwxDxT1ld5XLlyJYMGDeKuu+6Ki44W3tVwtOpfsfpPRFzBftgRkiRtF0KUhSufnPFqx0A/MFavB/+q4bT4fOxrbe2xjNtdicGQQWpqISZTAampo2I6midh7bV2LTPuu49pDgf5OTlMa2lhxn33hR55d7uPcOjQPVitH8Sk3+u4NObotd+r4SQPydZn7vV66HN/OSh6IHuPhuTi+naEe8pCDxw1GmqgRVxacd6xWnmsupqrRo6MuKg+LW0sbW21GI2ZmM2Bv5Zk2an4aJ6EtdeqVaFH3s0AZjOi/X1p/nxaWnbR1PQv3O4qhg+/IOa/3FTHpTFHr/1eDUerNtZCQ6/tpQbmKE8797Z8JM7KlSvjrhMr4uWlrzXUQA9xJWe82hG8B643jhoNNdAiLq04fiHINBop62H6ubBwKUK04fc78Xg8+P1OhPBQWKhsoW3c2+urr+Cuu2D3bsjIAMBXX4/7yBEOSRKew4cByM5eQH7+YsaP/1OvB12K4uojjl77vRqOVm2shYZe20sNgsfCJKq8lhwtNPTaXlpoxDuu5IxXO0Z13d9FJxw1GmqgRVxaca7Ny+OyESNI6+EpnEGDpmAwZCDLrRgMbaSmFlFYuJTsbGULbePSXrIMwRiPHYMNGwKvW1shNRWTzYbw+UhLSaG+qIgxgCQZGT36tpi0Y45LBxy99ns1HK3aWAsNPbaX2nXKep3B6S+zPgPFu5r+l5zxake4Iwz0wFGjoQZaxJVIzlqrlXnl5VwJzCsv55PmZgw9zAg5HDsxGNLIzf0RJ520idLSdYoHXbHExdq1MG8evjFjAvsIrV0LX34J118PL7zw73Lz5sHPfgZ/+EPgEfe2Nry5uUg5OeSkpjL6178OW73H06A45l750Jij136vhqNVG2uhobf2SktLw2q1qvrPT68zOP1l1mcgeBdCYLVau21UGw26nvGSJGkhsHDs2LG0traGTqFPSUnB5XKRlZVFU1MTI0aMoK6ujvz8fGpqahg1alTotPETJ04wbNgwHA4H6enpobO0jEYjHo+HjIwM7HY7ubm51NXVkZeXF6oj+Lu+vp7s7GxsNhuZmZm0tbVhMpnIysrCZrORmpqK0+lkyJAhWK1WRo4c2a2Ouro6cnNzMZlMeDweWltbMZvNUT11jKe2tpa8vLyonmRZxmKxMHz4cBoaGhR5Sk9PD+1v4vP5onrKz88PeQpuShfN08iRI6mtre2Up2ieBg8eTHNzcyhP4Tz93/79PNDYiOz3YxKCY62t3L5/P78dPZr52dlhPfn9M5g8+V3q6o51qkupp+zsbBoaGsL2vaAn2+uvM+T++/GbTEjDhuE/fhzpttvw3Xorhl27kNxumhYtCnhqbibv2msDdTzxBG0PPYS5pgbPmDGkLFtG88yZZLrdob7n97dy9Oi9eDwHKSh4heHDR2NtP1YoUt8L5ykjIwOXyxX1+9QxT9nZ2TQ2Nvb4feqap451Rfo+dcxTSkoKPp8v6vepoydZlnE4HIqvEbW1tYwcOZKGhgZF14hYvk8dPaWnp+N2uxVfIxobGxk+fDjNzc2KrhFBT4Dia0TwbD9ZlmPylJWVhdPpVHSN6OjJYrEoukaMGDEiNIhSco0YOnQoVqsVu91OWlpaaJNPWZYxGo34/f5uv4OfGwyGbgO24GaZ4eoIbgiqpI7gU3A9xdExnmD5WOIJ/na5XJ28K6mjoxel7aKFp1jaOPiZ2+0mPT1dVa5760mSJEwmEwUFBd3+z+0RkU7P1tPP9OnTRaxYv359TOXDndKuB44ajVi9q9HRU3vN3blTTPziCzF60yaRun69GPP552LiF1+IuTt39mlcYu5cISZOFGLUKOHLzxdi+vTA67lzhfjwQyFcLuUaR44I+fbbhWhsFEIIIcuyqKi4WezYcbaw27eHiiX7fWzQqxct/Cdzr888xspJ9vv1CddQwwG2iQhjmuStxnYMGzZMlxw1GmqgRVyJ4lS63WQYDJgkCTMwyGgkw2CgKsLxEaLLXz4J81JZGViz1dyMoaX90O2MjMDZcOefD1Gmp4MaHllm+4oVHF6/Hv+TTwKBv7ROOmk5kye/S1bWtJjjj8lHH3D02u/VcLRqYy009NpeaqDXPPaX3A9k79GQHHi1I9wRCXrgqNFQAy3iShQneMxOTkoK+UC6wUCrLFMUYWBTWXkf+/ffSmvrgYTGxdixgQX0BQXIwd3zW1shzFEePWmYDQaeuvFGPpgzh5033RT6PC2tEJNpcKyhh9XQG0ev/V4NR6s21kJDr+2lBnrNY3/J/UD2Hg3JgVc71BzKqgVHi4OC1ejoqb2Cx+w4/X4E4PT78QjB0jB7eMmyD5ttM3b71tBB2AnzEjwbzmBAGjw4cEZcDGfDddT46dSpnLdqFdNHjuxWTghBU9M62tpi33hST3nsrYYa6NWLFv6TuddnHvtL7gey92hIDrzaodddibXYyVeNjp7aa352Nv9ZVESu2YwDyE9N5Ynx40ML6zvCYDAxefK7jBv3e8zm3MTEJcvw+OMweXKns+HIz4/pbLiOGjMGD+aUjIzA3l2yDM8+C+XlANTUPE1FxS3s3Pld4MrQgd+99tGHHL32ezUcPeyUHS8NvbaXGug1j/0l9wPZezTo+qlGLaFmM0otOPHYJDMROnprr8/tdlyyzHLgttLSHsuaTIMZOvR7iYvrzTfhz3+Gjz+Gv/0N5s/HqeI8tYga770XGHi99Ra8/TYGQyZe73FMpuFAFm1ttRw4sAR4IuoWGXrLY2801ECvXrTwn8y9PvPYX3I/kL1HQ3Lg1Q6j0ahLjhoNNdAirkRyBCABo2NWSEBc8+fD1q2wcCG0bykQD40ql4una2oYVFLC8nPOgUsugdRU6utfITX1JIzGTDweB0ZjJn4/VFevijrw0lsee6OhBnr1ooX/ZO71mcf+kvuB7D0akrca26HXzfG02FBOjY7e2uvpU07h09NOY0gPZVpavmLPnqtoaFiT2LgGDYLf/hZmz46rhtlg4F9NTfzTbsfz8MNwxhnAvw/8/jf8ig/81lsee6OhBnr18m3bSDKeHL3mXq/tpQZ6zWN/8R4NyYFXOzIyMqIX6gOOGg010CKuRHNSezgiCMBm20hr637a2o7GPy6fD/7xj8D6KyXlVWiMSk3lwbFjWVNcjLmD18GWPAZvqG9/1YrLdRCfz6rowG895lGthhro1YsW/pO512ce+0vuB7L3aEgOvNpht9t1yVGjoQZaxJUojifCYKcr8vNvZvz4x8nJuTT+cT35JDzwADz8sLLyajSAi7KzGdnx3LDGRsb/l4Mxj9cz8drdzLyyhpKfO8naZFF04Lee8thbDTXQqxct/Cdzr8889pfcD2Tv0ZAceLVj+PDhuuSo0VADLeJKFOdHe/bww927aYgyHWwwmBky5Lukp4+Nf1zTpsGwYbBggbLyajTCFyKlbC6p9lRMrRK+LIm0JjOTnsgi+8s4afQBR6/9Xg1HqzbWQkOv7aUGes1jf8n9QPYeDcmBVzsaGmI/bFgLjhoNNdAirkRwHD4fdR4PtR4P2SkpMdcft7hmz4Z334UIT1TG0/unzc38ZN8+PmxsDLyxezeGUYWk5ZwK0ndIzy7BmD4UVq1CCLnHA4T1ksd4aKiBXr1o4T+Ze33msb/kfiB7j4bkwKsdUQ+17COOGg010CKuRHCyTCY2lJbyysSJGHt45Pfw4Xs5enQVXm9j/OJyu+HYsX+/2cMme/H0XufxsMPh4J/BgVdlZeAooiDq68FkQlQe5uDBX9DQ8JomccWTo9d+r4ajVRtroaHX9lIDveaxv+R+IHuPBl0PvCRJWihJ0rONjY20trbicDiw2+24XC4aGxvxer00NDQghKC2thYInGgPgQ3PhBA0NDTg9XppbGzE5XJht9txOBy0trbS3NyMx+PBYrFw7Ngx6urqOtUR/F1fX4/P58NqteJ2u7HZbDidTg4fPozNZsPtdmO1WvH5fNTX14eto66uDlmWqaiowOPx0NzcrMhTTU1NqI7a2lpFnmRZxmKxIMuyYk8HDx7E6XTidDoVeaqpqQl5slgsijwdP368W56ieaqsrOyUp3CeLHV1nJyeTn19PUKIbnmy2aqxWD6koeFNmpud3Tx1bGOlno4eOULLihWIq67C8u67UT1VVVVF7HuR8lRTUxO2752Rmsp9+fn8cvhw3G43noIChNOJ1+vFZLcjW61QVYVr5CBsts85cuR/8Hpbwno6ePCgou9TR09Hjx6N+n3q6qljG0f6PnXse/v27VP0feroSZblmK4RtbW1HD9+XPE1Iuipoxclng4ePBjTNcJisXD06FHF14hYvk8dPVVUVMR0jbDZbBw+fFjxNSLo6dixY4qvEUIIdu/eHbMnv98f9fvU1dORI0fC9r1Inr755puYrhFer5e9e/fGnKdDhw4pvkZA4P+6aN+nrp6CXmK57u3fvz+mvrd///6YrhEd41JyjQh6Cm5uquQa0drayqFDh2L+Pu3duzema0Q0SD3dhtALysrKxLZt22LibNiwgTlz5iQmIJ0j6X1Op/eEkHE6d+N2V5KT8x+9E1i7FlatCswyyTJkZgZuMY4b17t6exvTkiVgNuPw+ciyWgMzYM89R8P0FoYMOZPU1FF9F59GGMj9Hga2/6T3OX0dRp9Br/4lSdouhCgL95muZ7y0RHCEqzeOGg010CKueHN8ssyVe/bwYFUVcg9/QEiSgUGDJkccdCmOKzjAqa3Fk5UVGNy0tMCBA1GpCW2v+fNDRxOZWloC68yeew7mzyc393JSpZy+iasXHL32ezUcrdpYCw29tpca6DWP/SX3A9l7NCQHXu0YNSr2GQEtOGo01ECLuOLN2e9ycaC1lV0tLRh6caSD4rhWrYKUFMjMxGw2B2a7MjMD78dLIwbOS7W1XLlnD81eb2DwtW4dW157Ddat+/d5kLt3B3a437WLxsZ/Ul39WMLjigdHr/1eDUerNtZCQ6/tpQZ6zWN/yf1A9h4NyYFXO4L3vvXGUaOhBlrEFW/OKenp/HnSJO4eMyZiGZvtcyorl2O3R75VrTiuykqw26G6Gq/LFXgvIwOqqqJSE9FeO1paONDayobm5siF3nkHGhrw/e0vVFU9QH39q53aQg95jJeGGujVixb+k7nXZx77S+4HsvdoSJ7V2I7s7GxdctRoqIEWccWbYzIYODUzs0d+Y+OHWK1rSU8fx+DBYW+3K4+roAC2bweDAdOIEYH3WluhqCgqNRHttTgvjytzczm9p8O3ly2DceMwXXopY5rXIsutZGVNT2hc8eDotd+r4WjVxlpo6LW91ECveewvuR/I3qMhOePVDpvNpkuOGg010CIurTgdkZ9/EwUFP2fo0HN6r3HffZCbC8OH4zcawekEjweWRt8hPhHeT8vK4rtDhmDq6agkgwF++EMwmcjJWUhu9mVI7U+NJSqueHD02u/VcPqi3ydKQ6/tpQZ6zWN/yf1A9h4NyYFXOzKjzJz0FUeNhhpoEVc8OSc8HpZXVvKexdIjPy2tkLy8a0hLK+h9XPPnw9NPw7hxGGw2yM8PLGoPrqfqAVq1V4+QZVi5Em68kcY3H2DLlgns2zed8vJ5WK1rExpXf+n3aji6yH2cNPTaXmqg1zz2l9wPZO/RkBx4taOtrU2XHDUaaqBFXPHk7GppYa3VykdNTTHXqVQjhAMHYP36wL/bF7E7du3qvIi9txoqOY1eL789epR7Dh2KXqEkwYgReA1OrJse5pRbDzDtsmOMvWkLDX++QfHgayD3ezUcrb4rWmjotb3UQK957C+5H8jeoyE58GqHyRT7cjctOGo01ECLuOLJKcnMZNmYMfwgJ/xWCUIIqqr+kxMn/oYs93yGY49x+f3wm98Ebie+/bYyTqwaveCkGQy8bbHwr6amqGdVIklwxx0cv1imcLXA3CjhG2zCbPUz9tFGml9bFre4esvRa79Xw9Hqu6KFhl7bSw30msf+kvuB7D1qfXGtLYkkNEJeaiqX5eZG/NzlOoTF8h422yZyci5RLyRJcPHFgeOBzj9ffT0JQobRyH1jxvCd9HRGKDmrUpIY9rdKRIoRMswY3DIAIsVAzkv7YUmCA04iiSSSGODokxkvSZLulCRptyRJ30iS9JokSWl9EUdH+Hw+XXLUaKiBFnFpxQEwm0dSVLSC/PybkKSeu3mPGsHF6a+/3uk8RD2118U5OVS53ZyzaxdXAvPKy1lrtUYsn1YH/jSQPDKpdR5MzT78aYH34xlXbzh67fdqOFr2+0Rr6LW91ECveewvuR/I3qNB84GXJEmjgZ8BZUKIEsAI/EjrOLoiNTVVlxw1GmqgRVzx4lS6XLxWX8+h4F5aYWAyZZGT8x/k5v5QXVxCBLaKCKLLk4N6aq+1VitLDhygtq2NLKC2rY0lBw5EHHxJJ5+Cwe1HGAVCAtnox+DyIp18Slzj6g1Hr/1eDUer74oWGnptLzXQax77S+4Hsvdo6Ks1XiYgXZIkE5ABaHNGRA9wOp265KjRUAMt4ooXZ6PNxu+qq1nd0BBzfUo1WLsWLrsMPv9cOSdWjThxVlVXI4Sg2e+nBcg0GjFLEquqq8OWN9/3CKnkYPQYcY+W8A0Cg9+E6d7/imtcveHotd+r4Wj1XdFCQ6/tpQZ6zWN/yf1A9h4Nmg+8hBDHgUeBo0AtYBNCfKR1HF0xZMgQXXLUaKiBFnHFizMhI4OFOTmcGaG+pqb11Na+SFubsvF82Lg+/BAaGqCxUTknVo04cSrdbkyShN3no6X9vQyDgSq3Ozxh/nyMT79I2tiZZHoLSSkoxvj0yxguujiucfWGo9d+r4aj1XdFCw29tpca6DWP/SX3A9l7NEiih8OFEwFJkoYBbwJXAM3AGuANIcT/dSn3E+AnACNHjpz++uuvx6TT0tLCoEGDFJf3+XwxP7mgBUeNRqzetYpLC07A+wvAHuB64Ax1GrLMsJ07aZo2LbDAvpdxJdL7nYAV8AJpfj9moxE3MBx4rEfmvzUyqqpIsduxTZkSt7h6w9Frv1fD0eq7krzmDeRrXrLf6y2uuXPnbhdChD0upS8GXpcDFwohbmx/fS0wSwhxWyROWVmZ2LYt8ll74bBhwwbmzJnTm1C/tRjo3ktLjTQ3r2P06J+TkjK0r0NKOIJrvMyShN/lwpiejkcInhg/nvlKjrr46itYvBhyc5HffhODOT3xQScAA7nfw8D2n/Q+p6/D6DPo1b8kSREHXn2xxusoMEuSpAxJkiTgHGBvH8TRCTU1sS8z04KjRkMNtIgrHpzDLhdftbTgkeWInKFDZ1NU9IDiQVdIo7UVnnqq86J6hXHFu3wsnPnZ2Twxfjz5qak4gPzUVMWDrpqaGpg8Gd+kk2gobeLIgeVxi6s3HL32ezUcrb4rWmjotb3UQK957C+5H8jeo6Ev1nhtAd4AdgBft8fwrNZxdMWoUaN0yVGjoQZaxBUPzpoTJ1hcUcFf4nhafEjj6afhxRfh17+OOa54l4+VMz87m3WlpfwKOCU9nTFpynZoGTVqFEgS8vNPUf0DPzbvDvz+yE+LxhqXWo5e+70ajlbfFS009NpeaqDXPPaX3A9k79HQJ081CiEeEEJMFEKUCCGuEUJoc0ZED9DrXzPJv/46c0akpHByejpTutzTt1rXsnPnXOD7bN9+OhbL+7FrLFwIU6bALbfEHFe8y6vlHAa2ORx8EOGhgEga5tSRjBv3GJMnv4fR2POtxoHc79Vw9PAXdrw09NpeaqDXPPaX3A9k79Gg+RovNUiu8YoNA827Y+4HTgAAIABJREFU1bqWAweWIISftrZajMZUUlJGMH78E2RnKztLMQQhwi6m/7bgLxs2MKikhLlDh5IV6zEXsgybNsGRI/DjHycmwARioPX7rhjI/pPe5/R1GH0GvfrX2xovXaKuTuG23Rpz1GiogRZxJYpTXb0KSTJjMg0GhpCSkoMkmamuXtUzce1amDcPX0EBzJsXeK1w0KXX9hoF/EdOjuJBVyeN+nr41a8QTzyBt+5wXOPqL/1eDUer3Guhodf2UgO95rG/5H4ge48GRQMvSZIMkiSdJknSAkmS5kmSNDKuUegAuT2c+9eXHDUaaqBFXL3lNHg8uP3+bmXc7koMhgwkyQwMw2QajsGQgdtdFbnitWthyRI4ehRjUxPs3h14vXZtzHElorxaTq808vPx/Mccjn/fT2Xtg3GNq7/0ezWcb0XudcTRa+712l5qoNc89hfv0dDjwEuSpO9IkvQscBB4BLgSuA34pyRJX0iSdIMU7SC8bwkaFa6J0ZqjRkMNtIirt5z/OnKEs8vL+dxm61QmLW0sstz5SURZbiUtrShyxatWgdkMgBACTKbA61VRZsnCxJWI8mo5ALIQvFhby+KKih6f/gynYbx3JScuTMdtrMfns8ctrv7S79VwtMx9ojX02l5qoNc89pfcD2Tv0RDtfsRDwNPALaLLYjBJknKBq4BrgFfiGlUfYPDgwbrkqNFQAy3i6i3HIwQCGNvlib3CwqXs338rsuwCjPj9ToTwUFi4NHLFlZUwfHjg1mJqamDQZTBAVVVCvGjVXgAGSWJdUxMVra18ZrMxb9gwxRpGYwYTJjxHWtp3MBjCXx4Gcr9Xw9Ey94nW0Gt7qYFe89hfcj+QvUdDj7NVQogrhRCfdh10tX/WIIT4gxDiWz/oAmhVsHdTX3DUaKiBFnH1lvP0KaewobSUvPaZqiCys+eTm3sVPl8j0EBqan70hfVjx4b265LNZjAaA6+LimKOKxHl1XKCuHX0aP4wbhyzoxx1EU4jwzQWw3v/gAceCDxsEIe4+ku/V8PROveJ1NBre6mBXvPYX3I/kL1Hg9I1XpdLkpTV/u9fS5L0N0mSpsU1kj6Guct/5nrhqNFQAy3iigcnw2hECrMAftiwOeTm/gi4h9LSddGfZrz5ZnA6welEgsC/PR5Y2sMsWQ9xxbu8Wk4QZw4ZwllDh5Ji6PkrHlZDluGPf0T84++0bet+jOpA7vdqOFrnPpEaem0vNdBrHvtL7gey92hQuj5ruRDCIUnSWcAFBG4tPh3XSPoY/jCLtvXAUaOhBlrE1RuOP8q2J8OHn8+ECc8CZymruLY2cItRkpCamiA/H554AuYr235Cr+0VF420NNpuvpzK62UOmp5GCDk6R41OHMurhV699FnudcDRa+712l5qoNc89hfv0aB0o5+g6gLgaSHEO5IkrYxrJH0MNfuZacHRap81LeLqDefHe/ciAY9+5zuMSk2NuZ5uSE2FvDx4/nla8vNjvoev1/bqCKvXy8t1ddh8Ph4cOzYmDfOPfkLL1+9hNKbh9Voxm0f0Kq7+0u/VcPoi94nS0Gt7qYFe89hfcj+QvUeD0hmv45Ik/Q/wQ+B9SZJSY+CqhiRJCyVJeraxsZHW1lYcDgd2ux2Xy0VjYyNer5eGhgaEENTW1gL/3mHW6/UihKChoQGv10tjYyMulwu73Y7D4aC1tZXm5mY8Hg8WiwWj0RjaqyNYR/B3fX09Pp8Pq9WK2+3GZrPhdDrxer3YbDbcbjdWqxWfz0d9+1E2Xeuoq6tDlmUcDgcej4fm5mZFnlJSUkJ11NbWKvIkyzIWiwVZlhV7crlcOJ1OnE6nIk8pKSkhTxaLRZEnk8nULU/RPPl8PmoaG6lsbeWAw8HwMHmqrt6H19tIfX09QohueQrr6bLL4MMPqcnK6tTGSj0BEfteOE9+vz9i34uUp5SUlIh9T0meLHV1rGlo4L36eixtbWE9BXPfte/V1TUwceIrDB36O8wpOZ08AVG/T109dWxjJZ5aWloUfZ86egp+v5ReI2prazGZTIqvEUFPHb0o8eRyuWK6RlgsFoQQiq8RsXyfOnpyOBwxXSNsNhter1fxNSLoyWg0Kr5GCCFoamqK2ZPf74/6ferqSZblmL5Pwe99LNc9u90ec548Ho/iawQE/q+L9RoR9BLLdS/4f57Svhfs+7Fc94JxxXLd83q9iq8Rra2teDyemL9Pdrs9pmtEVAghov4AGcClwPj21/nA+Uq48fiZPn26iBXr16+PqbzVao1ZQwuOGo1YvavR0bq9XD6fqHA6w5apq/uL2Lp1ujh69PeaeFfD0aq9uvr/W0OD2N3SImRZjl3D5xPihReEuPJKIdraehVXf+n3ajh9lftEaOi1vfrjNU8pkv1+fcI11HCAbSLCmCbaPl7DJUkaDqQBGwBr++s2ILYzfHSOrKwsXXLUaKiBFnH1hpNmNDIhIyNsGVluxWjMIC1tTPQKv/oKvvyy09N6es1jPHK/aMQITs3MDPtAQlQNgwHWrUPs20vr+y/0Kq7+0u/VcPoq94nQ0Gt7qYFe89hfcj+QvUdDtNuF2wkMsDr+3t7hdb9BcHpbbxw1GmqgRVyJ4uTn30hp6QaysxdGr/BPf4LbboP33kt4XL0pr5YTVw1JwvPTK9n3Mz/7T1qDLHtUx9Vf+r0azrcy933I0Wvu9dpeaqDXPPYX79HQ4+J6IUT4Fbn9ECNGjIheqA84ajTUQIu41HCyc3K4fu9exqWns2zMGEwRtkeQJEP7kUE9QJbhjDOgqQnOPbdXcem1vcKhwunkf+vrOTUzk6tHdj7tK5pGypkXIbJfJ8M4GJ+vCbN5pG7bSw306kUL/8lrnj7z2F9yP5C9R4PiBfKSJA2TJOl0SZK+F/yJayR9DL0egDrQD4z9orqab5xOvnQ4wg66um510CMMBli8GNasgQ63LfWax3jlvtHn48PGRt6xWGLWkCSJCROe55RTnsTMMNVx9Zd+r4ajh0N546Wh1/ZSA73msb/kfiB7jwZF20lIknQT8HOgACgHZgGbgXlxjaYPkZ+fr0uOGg010CIuNZwZhYW8mJ2NzecL+3l9/V9oaHiN/PzFjBjxA2WVdlnvpNc8xiv3p2dl8bOCAs4Nc3SQEg2DZIY//hHefhv+8hfdtpca6NWLFv6T1zx95rG/5H4ge48GpTNePwdmAEeEEHOB04ATcY2kjxF8fFRvHDUaaqBFXGo4lro6pgwaxOyhQ8N+7nLtx+OpB4w9V/TWW7B2LbQ/etzbuPTaXuFgMhi4Ni8v7P5nijQkCSwWZHsjjg+e1G17qYFevWjhP3nN02ce+0vuB7L3aFA68HILIdwAkiSlCiEqgAlxjaSPMWrUKF1y1GiogRZxJYJTVLSS4uI1DB06J3KhtrbArvTLl8OePZrE1RcaaqBUw3v95VSeX4f/T48wvGwMjtOHYV/9YNx11JZXi2Tu9cfRa+712l5qoNc89hfv0aB04HVMkqShwNvAPyVJegfQ5s8SjRDcOE1vHDUaaqBFXLFymrxe7vr6a/5htUYsI0kG0tPHkpISfkasvRDccUfgOKApU3odlxqOVv2rJ6xrauK2/fspdzhi1nBte5tRfz5BSqPAl2XC2NCK+Ze/UTz46i/9Xg1HD7mPl4Ze20sN9JrH/pL7gew9GhSt8RJCLGr/50pJktYDQ4AP4hpJHyMvL0+XHDUaaqBFXLFyvnY62dDWhsNiYUF2dsx6IZjNcMklgZ84xKWGo1X/6gm7nU6+tNspTE2ltH1fGqUa0u8eQ6SkQIYJg08g0iVkfEi/ewyuWBGV31/6vRqOHnIfLw29tpca6DWP/SX3A9l7NETbQHVw++/hHTZT/Rr4DBgU10j6GCdOxL5kTQuOGg010CKuWDnj09O5ecgQfhDhUd4TJ/7GoUN3Y7N9EXMsvYlLDUer/tUTvp+Tw69POonbR4+OWSPlmAORZiTF6iW12o2hTQ68PtaiiN9f+v3/Z+/Mw+Sq6vT/ObV1VVfvne6kO+mQACHsoEHcRxZBIouKSwbHcUZHcYu70VEHHFxAjYALLj9m3MZBQXQUF4IbwRGRTWEIhEBCOkmn03ulq7prr7rn90d1h0qnqu+9p27dPl2p93nqSbr6vPW+3/OeOn3r1r3nqHB0yN4pDV37SwW65lgr2R/NtZvB7IzXD4FLKCyYKgEx599jHXWzgGgvcceXDhwVDRW44csOZ8vEBJsHBuhPJlkdj5OXkvVzznrFYvdz8ODdtLbOs7LJtdfCMcfAa15z2BISqr5UOW6Nr/mwMhhkZTCopJFd0Yx3NHHoZ5HOIQwP2RVNBOfh2dVRba+KoyV7pzR07S8V6JpjrWR/NNduhnnPeEkpLxGFvUZeJqU8Vkq5uvhfR50sMKaKrnvRiaOioQI3fFnlbJmYYOPOnQyl07QIwVA6zcadO9ky51qv5cs3snr1p2lpObv0C+3bB//zP/D1r5e8m9Gur0o4bo2vamnID38QT9Yg3yBJ9Ery/jyerIH88Acd1VFtr4p69vpxdM1e1/5Sga451krtZjC9uH5ms8efOapqEUKIS4UQN0ciEdMd7efuKp7NZk13tC/eVbyhocHSjvbFO6VLKU13tJ+7U3oymTTd0b64plAoZGlH++KaZndkn29H+7k1ze4mP9+O9sWvEQqFLO1oX1xTMBg03dE+Eonwhb17wTBI5fNkKWwU6geu3b37sJoiER+dna9kfDx3qCYp5bM5NTeT/NznSF15JVEoW1NxH1utyefzme5oX1yTEMLSjvZz+7jc2LOa09ya0uk039y9m398/HFGo1FyuZyl91P8795O+oaryHSH8SUE2c4Gpj7/IVj/AUs1FfexlZpSqZTlmmZzMgzD1hwxNDREMBi0PEfM1lRci5Wastms7Zy8Xq/lOcJs7JWrKZlM2pojotEoUkrbY6+hocHyHCGlPPRHzk5N+Xze9P00tyaPx2Pr/RSLxWzNEdlslkQiYTsnwzAszxFQ+Ftnd46YrcXOvJdOp22NvXQ6ben9VKqP7cx72ZkP1VbmiEQigWEYtt9PiUTC1hxhinK7Zxc/gK8Dz7PSthqPdevW2d4Z3O6O5dFo1LaGGxwVDZXd6t3wZZWz6i9/kavuu082/vGPctmf/iTXPfSQfO6DD8rVf/mLKdeN2lU4bo0vK/W/5ckn5bqHHpJ3TUxU5ssGt1bGvQpHp+wr1dC1vxb7nFcJpz7ut1ZdQ4UDPCzLHNNYuqsROBd4hxBiLxBn5hovKeWR9+YvUog5q5nrwlHRUIEbvqxyVgeD7E4mafX5aJzhJAyDVUXXJx08eDep1F7a2s4lFFp15ItIecQK9ZX6qoTj1viygrf39JCVkhe0tJCOx23zRTxeWJ5j/3646y7w+805NTLuVTg6ZV+phq79pQJdc6yV7I/m2s1g9cBrvaOqGsLrNVn5fIE4KhoqcMOXVc6mvj427txJq8dDEIjn82SkZFNf36E2ExO/ZnLyjwQCy4488Mrl4E1vghe9CN7xDiixYruKr0o4bo0vK3hha+uh/+dUfLW2IhNx8okx0o//hvBzLjHn1Mi4V+HolH2lGrr2lwp0zbFWsj+aazeD1QVUPyul3Fv8AD7rqJMFRiaT0ZKjoqECN3xZ5azv7OSmNWvoaWggks/T09DATWvWHHZXY2fnJXR3b6Cp6YwjX+DBB2HXLvjf/7V0NkbXHN3IXtXX+Edfyv99ycdwy5+qoqPruFfh1Fr21ebomr2u/aUCXXOsldrNYPWM1ynFPwghvMA6R50sMBrLLDWw0BwVDRW44csqZ18qxTHBIL89/XSMXI5AIHBEm/b2c2lvP7f0C7zoRfDd70IyCR7zzxa65ljN7G8fGeET/f2MZbM8t7mZTX19RyzXMa+vM/6e0K4HSh/4luPYgK7jXoWjW/aVaOjaXyrQNcdayf5ort0MZguoflwIMQWcLoSIzTymgFHgDkedLDBm76bQjaOioQI3fFnl/Hh0lDc/+STfHxlRr/+00+DsMstMKPqqhOPW+LKCLRMTfLS/n/3pNOl8nv2pVMnlOubzFQgs5eSTf8jS7itg2nwR1VoZ9yocnbKvVEPX/lKBrjnWSvZHc+1mMFvH6zopZTOwWUrZMvNollJ2Sik/7qiTBUZHR4eWHBUNFbjhyyqnw+9nZTDI6eFwSc7U1CPEYg+SzxddGL5lC5x3Hi/4+7+H884r/Oywr0o4bo0vK9g8MEBQCJY3NHBsYyMtPh8BIdg8MGDP1//9H7zqVfBp8/0aa2Xcq3B0yr5SDV37SwW65lgr2R/NtZvB0jVetXaQVQqjo6NaclQ0VOCGL6uct/b08D+nnsrzWlpKcoaHv8fTT7+baPS+whNbtsDGjTAwgG9yErZtK/xs8eBL1xyrlX1/KkWjx0Obz4fPMABo9HjYk0rZ89XTgzywn/S2P5JLH7TGsQhdx70KR6fsK9XQtb9UoGuOtZL90Vy7GaxeXF/z0HUD1PqGsUdywuGTaWo6jXB45tLDzZsLG2EbBgLA5yv8vHmzq74WWsMqVgeDJGYOuPy+wmWec5frsOSru5u9nz2ZbdfkOBj7ozWOReg67lU4OmVfqYau/aUCXXOsleyP5trNUD/wmsHsSrW6cVQ0VOCGLyuc8UyGzMxBQTlOb+87OPHE79LQ0Ft4or+/sA9jezvJFSugu7vw8549jvmqlOPW+LKCTX19ZKQkns8TTafZk0oxmc8ftlyHVV9NZ/09LW3PJxDotsyxq1FNHG3ZV6qha3+pQNccayX7o7l2M5geeIkCni+EuFwI8ZqZ/7uzwp2L6O3t1ZKjoqECN3xZ4Xx2715e9uij3BeNWtdZvRoShQ2cpddbWEIikYBVqxzzVSnHrfFlBcXLdcSAjJS8sLnZ8l2Nxb6WLLmEE074Bq2Nz4N5brmulXGvwtEp+0o1dO0vFeiaY61kfzTXbgazuxovBHYC/w68ErgYuAbYOfO7moGun2aOtk9/CcMgW/S111xOOj1ILjfnDpNNmyCVgni8sGJ9PF44CNi0yTFflXJ0+JRVjPWdndx95pn8vq+Prx5/PDccf7y6r9tug1e8An71K+scuxpVwtGYfSUauvaXCnTNsVayP5prN4PZOl5fAV4updxT/KQQYjVwJ3CSo24WELp+mjnaPv3dvHYtU7kcTTMrBc/l7N17HbHY/axZ8xVaW19cePJlLyt8tTg5iS+fh5NPLhx0rbe24YKuObqR/WkrV3KaTc4RvsJhjOgEyQd/Svjyy61x7GpUCUdz9vU5T88cayX7o7l2M5h91egD9pd4fhAwXxJ8EWF213bdOCoaKnDDl1VOs893aG+suRwhvHg8AUKhojM0Tz4Jra1w8cU8cOutcPfdlg+67PiqhOPW+HJDYy7HOOfFbP+Unydf9xTp9LAjOrqOexVOLWdfDY6u2evaXyrQNcdaqd0MZgde3wEeEkJ8TAjxxpnHx4AHgG876qQEhBCXCiFujkQiJBIJpqamiMViJJNJIpEI2WyW0dFRpJQMDQ0Bz54SzGazSCkZHR0lm80SiURIJpPEYjGmpqZIJBJMTk4Wtj8ZH6e9vZ3h4eHDXmP235GREXK5HBMTE6RSKaLRKPF4nGAwSDQaJZVKMTExQS6XOxTQ3NcYHh7GmLloPJPJMDk5aammzs7OQ68xNDRkqSbDMBgfH8cwDMs1+Xw+4vE48XjcUk2dnZ2HahofH7dUU0dHxxE5FdeUzmSOqCkUCh2W03HH3UhPz4/x+7uf9dPTA7/9LWPveAdSyiNyMqupuI+t1tQys9RFqbFXKqdwOFx27JXLqbOzs+zYs5qTWU2BQIB4IsEP+vv52K5dDI+MmNbU0tJyWE5x6aPhpPWEw+eQyUyXrKm4j63UJISwXZNhGLbmiKGhITo6OizPEbM1FddipSafz2c7p+bmZstzhNnYK1cTYGuOiEajBINB22Ovvb3d8hwhpSSbzdquKZ/Pm76f5tbU1NRk6/0022d25j0ppe2cGhoaLM8RUPhbZ3eOmK3Fzrzn8XhsjT2Px2Pp/VSqj+3Me3PHi1lNDQ0Ntt9Ps+PS6hxhitkXLfcATgb+FfgacNPM/0824zn5WLdunbSLrVu32mo/Pj5uW8MNjoqG3dpVdKpR+4bHH5f/uH27HE6nlXXcqF2F49b4Uhn3hmHIVz32mFz30EPyb7FYZb7icfscB9pLWc++PufZg6451uc8e9B13AMPyzLHNKZ7NUoptwPbhRAdhR/l/CslLlKEw2EtOSoaKnDD13yceD7P7lQKnxB0+HwlOYaRw+OZM2QnJwtfM1Zwo62uObqRfTgcRgjBW3t6yEvJsaGQuq+rroLf/x5+8hNYvtwax66Gwzjas9eRo2v2uvaXCnTNsVZqN4PZXY0rhRC3CiFGKXy9+KAQYnTmuVWOOllgpNNpLTkqGipww9d8nLDXyz1nnsl/rl2Lv2hj62LOrl3v5fHHX0M8/mThCSnhyithwwbYX+pSxMp9OcVxa3ypaly2ZAmv6eqi1Wf6WaysL2kY5LPTJP7yY8scuxpOo569fhxds9e1v1Sga461UrsZzK7xug34GdAjpVwjpVwD9AA/B2511MkCw2fhD85CcFQ0VOCGLzNOo9fLyXM+WcxypJQkk8+QSg3g9y8p/HJ8HCKRwqOClYV1zdGN7J30Nf2PL+Sxz3vpP+m+2csUlHV0HfcqnKMheyc5umava3+pQNcca6V209cz+f0SKeVtxU9IKfPArUKIzzjqpI465oEQgtNOu5NUqp9AoKvwZFcX3HUX7NtX2Caojoowksnwm0iEJX4/r7S4mGoxwidcRCD/37S0PA8pswgRqILLOuqoo47FDbO/Vn8VQnwD+D4wMPNcH/BPwCPVNOY2crmclhwVDRW44ascJ2cYvP3ppzmpsZGP9PXhKbpeq5jj8fhobFxzONnng2OPte3Fii8nOW6Nr0o0diQSfHX/fk4Oh+c98Crny+PxccopMyfCk0kImXPsajiNevb6cXTNXtf+UoGuOdZK7WYwO/B6M/AvFFarXw4ICut6/QIXlpNwEw0NDVpyVDRU4Iavcpynk0m2TU8TzeUOO+iaV2dyEpqbYWah1Uqga45uZF+s8fzmZi5dsoSXtrZa5hyBsTH42McK+fz0p4dueqiVca/CWQzZ68TRNXtd+0sFuuZYK7WbYd5rvKSUGSnlN6WUF0kpT5NSnjrz/29IKd25AtIlxONxLTkqGipww1c5zvGhEDevXcsHV6woy+nvv4rduz9BOj2zdcMXv1hYJPXBB237sOrLSY5b46sSjaDXy6dWreK89nbLnCPQ2YkcGCB3YBep/oescexqOIh69vpxdM1e1/5Sga451krtZpj3jJcQwkfhjNerKZzxksAB4A7g21LKrKNuFhCtJp/yF4qjoqECN3yV4wQ8Hp7b3FyWI6XB5OQ95PNJ+vo2gWHAnj2Fi+r7+mz7sOrLSY5b48sNjXk5Hg8jnzybQd8Wuv330sfZSjq6jnsVzlGTvUMcXbPXtb9UoGuOtVK7GczuavwBcCaFrxqLN8k+A/hvR50sMCYmJrTkqGiowA1flXDWrv02q1d/Br+/HTweuOUW+PGPoafH9ms66UsnDRWU0tg2Pc1X9+9nusx1DWa+ms66gmD7CTQ0rLTMsavhFOrZ68fRNXtd+0sFuuZYK7Wbwewar+dKKdfOeW4/cL8Q4mlVUSFEG/CfwKkUzqK9VUr5F9XXcwJLly7VkqOioQI3fJXijGYy/MfQEGc3N3NBR0dZTmPjCTQ2nvDsL4So+KL6+Xw5zXFrfDmh8dXBQR6ZmuLExkYunCeTcgiHTylcZC8lpFIQDNbMuFfhLKbsdeDomr2u/aUCXXOsldrNYHbG66AQ4vVCiEPthBAeIcQGoJIV7L8C3CWlPJHC2bMnK3gtRzC7N5NuHBUNFbjhqxTn0elpfjY2xi/LfKI4gjM9DYmEbW27vpzmuDW+nNB4VWcnVyxdynFlVrE38yWEgPvvh9e8Br78ZUscuxpOoZ69fhxds9e1v1Sga461UrsZzA68/h54HTAihHh65izXMHD5zO9sQwjRAvwdM3dFzlzAP6nyWk6it7dXS46Khgrc8FWKc1JjI+9fsYJXLVlSljM4+C1GRm4hl4sVvl684ILCvw5B1xzdyL6UxiVLlvDhvr6yB16WfHV0IAf2kX7gTox8tmbGvQpnMWWvA0fX7HXtLxXommOt1G4GMXeF6bINheicaT9ekaAQZwI3A9spnO36K/B+KWV8TrsrgSsBli5duu7WW+0tlD89PU1TU5Pl9tlsFr/fb0vDDY6Kht3a3fKlxknh938EyAI3cMz3f8KS++5j58aNxE477Yj2btSuwnGrv7QZ91LStOtjTB83CZ73kc2urYlxr8I56rKvkKNr9rr2V33c6znuzz333L9KKc8q9TvLB16HkYQ4CxiSUg4qcu8HXiylfEAI8RUgJqW8qhznrLPOkg8//LAtnXvuuYdzzjnHrr2aQC3VbhgZJibuJJMZZPny9xSeHBwsbBFUYg2vWqpdBU7VnzEMHozFmM7nuUhhFXuAoaHvEIs9SE/P22hpKTn/OIp69kdv/fXaz1loGwsGXesXQpQ98DL7qrEc3gv8Sghxm2nLI7Ef2C+lfGDm558Az1X04RiGh4e15KhoqMANX3M5u5NJfjY2xkAqVZYzOhqhq+vVzx50ASxf7sjCqeV8VYPj1vhySmNXMskHdu3iq4ODR+y7aNXXsmVvoefRXsT5F5Dq8TF1djux2z5dkS+nUc9eP46u2evaXyrQNcdaqd0MSgdeUsp/klI+B3ibAncYGBBCzN4teT6Frx0XFN3d3VpyVDRU4IavuZx7Jif53N69/Gh01JyTy8E87SqBrjm6kX05jRMbG3lRayuXL1lCds6Bl1VfUz/+DKF3XkNoWxTp9+IdTRD40GcsHXzpOu415KpZAAAgAElEQVRVOIst+4Xm6Jq9rv2lAl1zrJXazWB64CWEaBVCbBBCfEgI8cGZ/7cBSCmnFHXfC9wihHiMwjph1yq+jmOIRCJaclQ0VOCGr7mc40MhLujo4AUtLWU5e/fextTUIxj3/gkuvhiuu862rl1f1eC4Nb6c0vAIwVfXrOFtvb0EPB5LnLkQ19+I4RUIAZ50Htnow/B7ENffqOzLadSz14+ja/a69pcKdM2xVmo3g9nK9W8GPgX8Fpi9nutc4FohxDVSyv9SEZVSPgpU/6IPG2iZ54//QnJUNFTghq+5nL9ra+Pv2trKti9c3/VlIhGD5+x/d2FDbAcWTDXzVQ2OW+PLDQ2rHP/+KXLtPnItGYyAxINEBr34909XxZcK6tnrx9E1e137SwW65lgrtZvBbAHVTwLr5i73IIRoBx4AlA68dEQikSAQCGjHUdFQgRu+7HIMI0Vz84X4fGk8b3oLXPbaQ5suOwldc3QjezONaC7HvdEoZzU3s3SmnVVf2RXNeEcTGMGZ6/EkiFSe7IomghX6cgr17OtzXrXau8mxC11zrJXazWD2VaOgsLL8XBgzv6sZqHSqGxw3JiAVnUpr708meTqRwJjnrlqfr4W+vo9z3HFfLDzR0gJl9nSsBLrm6Eb2Zhpf2LePT/X384eDz66XbNWX/PAH8WQNPCkPAh+eZA5P1kB++IMV+3IK9ez14+iava79pQJdc6yV2s1gdsbrc8DfhBC/BQZmnlsJXAB8xlEnC4x8Pq8lR0VDBW74Kub8YGSEX4yP8+G+Pq6YZzuGfD4P+/bBypVl21QKXXN0I3szjfPb25nM5Vje0GCZM4uWDVcTA8SXbiT0ZBSEh+SXN9Gy4eqKfTmFevb6cXTNXtf+UoGuOdZK7WaY94yXlPL7FK7F+iOQBjLAPcBZUsrvOepkgaGynpkbHBUNFbjhq5jT4fPR29DA6WUWvpuY2MJf//p8dtx9IomLTiV1xQVgGLY17fqqFset8eW0xvnt7XzjhBN4WdG1eHZ8tWy4muaHDpL/p3chTjgJb/cxjvhyCvXs9ePomr2u/aUCXXOsldrNYHbGCynlQSHEVmA5ha8dD0gpK9mnUUvYXZXWLY6Khgrc8FXM2bhiBRtXrCg5oCcmtrBz53tIpw/Qsl+SbfQxFXyY0MHf0Nm53rauHV/V4rg1vtzQUOHE/vkFDL7qXhr6/sJJvLMqGiqoZ68fR9fsde0vFeiaY63UboZ5z3gJIc4UQtxP4SzXF4DNwB+FEPcLIRZ80VMnkUwmteSoaKjADV+lOKLExfIDA5sBL15vmOnTw+y66USGr+iaed556JqjG9lb1TiQTvO/k5O2OMUQq8/H27OacPhUpDQ/ba/ruFfhLPbs3ebomr2u/aUCXXOsldrNYHbG63vAO4pWmQdACPEC4LsU9lqsCTQrXLTtBkdFQwVu+JrljGcytPl8+Dylj/tTqX683g58vlaklEghkO3NpFJ7bGva8VVNjlvjqxoakWyWy7ZtI+jx8Iczz1Ty1draRWfnzwoH2qkUBOffeUDXca/CWczZLwRH1+x17S8V6JpjrdRuBrO7GsNzD7oApJT3A2FHnSwwDh60/+2pGxwVDRW44WuW84n+fl726KM8OlV6/d1gcDXN946y9q07OO1V2zjhXU/TfO8oweAq25p2fFWT49b4qoZGh9/P81paeGlbG7FcTrkWMTYG//Iv8A//ACbXTOg67lU4izn7heDomr2u/aUCXXOsldrNYHbgtUUI8euZ1epfNPPYIIT4NXCXo05KQAhxqRDi5kgkQiKRYGpqilgsRjKZJBKJkM1mGR0dRUrJ0NAQAAcOHAAKu4lLKRkdHSWbzRKJREgmk8RiMaampkgkEkxOTpLJZBgfH6ezs/PQfkyzrzH778jICLlcjomJCVKpFNFolHg8TmNjI9FolFQqxcTEBLlcjpGRkZKvMTw8jGEYCCHIZDJMTk5aqqmrq+vQawwNDVmqyTAMxsfHMQzDck2BQIB4PE48HrdUU1dX16GaxsfHLdW0ZMkSDhw4wHQ+z3Q6zapgsGRNfY+/hFWbhwg9ncAXzeEfSbHqS8Mse+Rs05qklEfkZFZTcR9bramtra3s2CtVU1NTU9mxV66mrq6usmPPak5mNTU0NFh6P13V1MS1q1cjJydpa2szfT/Nramrq4sD6TRy3z5Se58kvuv+eWvyeDy2azIMw9YcMTQ0xJIlSyzPEbM1FY8XszkiHo8TCARs59Ta2mp5jjAbe+VqEkLYmiOi0SiNjY22x15nZ6flOUJKSS6Xs11TPp83fT/Nram5udnW+ymbzdqaI2bb280pFApZniNmfdmdI2a92Zn3fD6frbHn8/lszRHFvuzMe3NrMaspFArZfj/NerMz780LKeW8D2A98C3gl8CvZv7/SjOek49169ZJu9i6daut9gcOHLCt4QZHRcNu7So6ldYezWbLtsu97MUysdwns01CZpqFjJ/UJLNrVkh57rmmGm7UrsJxa3zpPu6H/vBx+dc/PUfu23e94xr17LdWXUPX/loMc161OPVxv7XqGioc4GFZ5pjGyl2NW4At5odwixs9ClvRuMFR0VCBG76KOS2+eYZe/26M5gC5tiaCvh78Pl/hq6k9e2xr2vVVLY5b46uaGneOj/PpvXs5kMlw/MgIm/r6WN/ZaUun6ew3EtjzNA0NKxzzVQnq2evH0TV7XftLBbrmWCu1m8F0k+xyEELc7KSRhcbsqUXdOCoaKnDD14EDB+ZdqX4W3uNOpJE+Ag09ZGbbJxKwapVtTau+qs1xa3xVS2PLxAT/tGMHj05Pk8vlGEqn2bhzJ1smJmzphMOncMopt9Pd9XqY504hXce9CmexZ+82R9fsde0vFeiaY63Ubgaz5SQ6yjw6gVc66mSB0dvbqyVHRUMFbvjq7e3ltU88wVt37ODgzPfyJXHFFYhUGk8iTcDng3gcMhnYtMm2plVf1ea4Nb6qpbF5YIAmr5cGj4eAz0fY6yUgBJsHBszJRTpCCMTf/gavfS188YsV+6oU9ez14+iava79pQJdc6yV2s1gdsZrDHgY+GvR4+GZR7ejThYYsxfV6cZR0VCBG74eHxhgIJWiP5WitdxXjYYBP/85hMPQ3k5ubAx6euCmm2C984ungr45upG9VY3+VIoOn481oRCtM881ejzsSaXs63R1IfftIf3QbzBypc966TruVTiLPXu3Obpmr2t/qUDXHGuldjOYXeO1GzhfSrlv7i+EENY+6i4SLFu2TEuOioYK3PB1yooV3N3Tw/50Gk+JhVMBpvbeTTa4nca+doK/vhevEFCmrVPQNUc3sreqsToYZCidJuz1HlrFOWEYrAoG7eusXMmeT65koneAY2N/pqPj5cq+KkU9e/04umava3+pQNcca6V2M5id8foy0F7md+W/J1iEGBsb05KjoqECN3yNjY3R4vNxcrj8EnDTwb3s/ngnYze8FjweV+rXNUedat/U10dGSuL5PJlslvFsloyUbOrrU9JpfNEGmtvW4fWW3qtT13Gvwlns2bvN0TV7XftLBbrmWCu1m2HeM15Syq/P87uvOepkgdHeXu74cmE5KhoqcMOXFc7SpW+mufn5+HzNyjp2oWuOOtW+vrOTmyhc6/VgLIZHCK455hjLdzXO1enu/nuWLr0CcrnC9XuBgJKvSlHPXj+Ortnr2l8q0DXHWqndDMp3NdYapsqsor7QHBUNFVTbVzKf55+2b+cr+/eX3+n9//4Pz8QkTU2nEgweo6SjAl1z1K329Z2d3H3mmXymp4eLOjo4q6VFWUcIAbffDhddBHfeWZGvSlDPXj+Ortnr2l8q0DXHWqndDPUDrxmEQiEtOSoaKqi2r+2JBDsyGR6KxUpujI1hwFVXwcUXw44dyjoq0DVHXWt/2/Ll3Hbyyby0ra0ynYYGjIPjJO77iSO+VFDPXj+Ortnr2l8q0DXHWqndDGbLSbx45t8GR1U1RHa+5Q0WkKOioYJq+toyMcGHd+1iKJ3myUSi9NpP09MkV4WIt8eILZtU9qUCXXPUtXaZy5U+eLapkz/3JTz5SQ9PvmEHmcxoxb5UUM9eP46u2evaXyrQNcdaqd0MZme8vjrz718cVdUQdv+QuMVR0VBBtXxtmZhg486djGQyLPP7SeTzpRfebGlh+IMnsuOaMKnMXmVfKtA1R91rn87l+HM0qqzjbe6g8TmX0NZ+PoaRMm1fDdSz14+ja/a69pcKdM2xVmo3g9lyElkhxHeB5UKIr879pZTyfY66WUB4vV4tOSoaKqiWr80DAwSEIOz1YhgGYY8H8nk2DwwccWH2smVvJhw+hZaWFyr7UoGuOepce9owuHjbNpKGwa9PO42uORfHW9VZteqawqSWSFTsSwX17PXj6Jq9rv2lAl1zrJXazWB2xusS4DdAisMXUZ191AwymYyWHBUNFVTLV38qhU8IhjMZpvJ5oMTCmw8+CLt2EQodR3f3GwgGn12iwI36dc1R59obPB6e39LCc5uaiOZyyjpCCLj2Wnj5y2H37op8qaCevX4cXbPXtb9UoGuOtVK7GeY98JJSjkspbwUuk1J+f+7DUSclIIS4VAhxcyQSIZFIMDU1RSwWI5lMEolEyGazjI6OIqU8tLLs7J5K2WwWKSWjo6Nks1kikQjJZJJYLMbU1BSJRILJyUkymQzj4+MEg0GGh4cPe43Zf0dGRsjlckxMTJBKpYhGo8TjcaSURKNRUqkUExMT5HI5RkZGSr7G8PAwhmGQTCbJZDJMTk5aqqmxsfHQawwNDVmqyTAMxsfHMQzDck25XI54PE48HrdUU2Nj46GaxsfHy9a0wuvlYC5HJJtlyjDIZrMkDIOemVO3QwcOIK+7juzrXkf2wQePqEkIcVhOZjVJKY/Iyaym4j62UlMkEsHv95cde6Vy8ng8ZcdeuZoaGxvLjj2rOZnVlM/nLb2fimvy+/1EIhGu7unhiz09LM3nTWsq7uO5NSUTCbKJSQ5u/a9DNaXTads1GYZha44YGhoiFApZniNmayquxWyOiMfj5HI52zn5fD7Lc4TZ2CtXUzKZtDVHRKNRpJS2x14wGDR9PxXXNHsHmZ2a8vm85Tlitiav12vr/RSLxWzNEdlslkQiYTsnwzAszxFQ+Ftnd46YrcXOvJfJZGyNvdkxZmfem/VlZ96bvf7K6rxnGIbt91MikbA1R5hCSmn6AFYAPwNGgRHgp8AKK1wnHuvWrZN2sXXrVlvtx8bGbGu4wVHRsFu7io7V9neOj8u+++6Ty+69Vx57773yxPvvl8f+5S/yzvHxQoNEQsovfEGmXvVSOXLghzKdHq7Ilxu1q3DcGl+LcdxPP/07+chdp8vHHrtUGoahrFHPfmvVNXTtL53mPLc59XG/teoaKhzgYVnmmMbqchLfBX4B9ALLgV/OPFcz6Ojo0JKjoqGCavla39nJ/zvhBE4KhzE8HnoaGrhpzZpnr+8KheCjH6X/uuPYN3g9yeQzFflSga45Lpbah9JpHpr5pKqi03j8eQT6TqG19cWHLrLXddyrcGo5+2pwdM1e1/5Sga451krtZrB64NUtpfyulDI38/ge0OWokwXG6OioeaMF4KhoqKCavmYX3vzL6tXcfeaZJVc7X9L1Gjo7LyYcPq0iXyrQNcfFUPuuRIJLt23j6j17MMotjGuiI4SHk066hZUrP4Y3LZV9qaCevX4cXbPXtb9UoGuOtVK7GaweeI0JId4khPDOPN4ElFiMafFC1w1QF/uGsTvicX45Ps5wOn0k59574YEHwDBYsuQyVq++5tBWQaq+VKBrjouh9uNCIY4LhXhOUxPTMzdPqOiIeByuvBJe/WrI5bQd9yqcWs2+Whxds9e1v1Sga461UrsZrB54vRV4AzAMDAGvm3muZjB7MZ1uHBUNFVTL15ZIhGv27OHn4+OHc6SEL38Z3vMe+POfHfOlAl1zXAy1CyH40cknc+2xx9LiK786jalOOIw8GCEXHSK57XfajnsVTq1mXy2Ortnr2l8q0DXHWqndDGbreAEgpdwHXOaosmbo7e3VkqOioYJq+TolHObc9nbObmmht7nobFYuV9in7957mTwpg296G42NJ+HxHD4k3ahf1xwXS+0eC4sLmuoIwfiHns9A7hnaWu/l2N71tn2poJ69fpzFPuctBMcNDV37yw0Np33V92qcga6fZhb7p78LOzrYfNxxPLe5+XCO3w9vexvyu99l7+AX2LHjLWQyQxX7UoGuOS6m2qWUbI/H2R6PK+u0PPcfCLSvprFxrbbjXoVT69k7zdE1e137SwW65lgrtZvB0hmvowG6fpqp9U9/hpGmtfWlpFJ7aGhY4YiOE76c5ujwKcspjVKcLZEIV/f388LWVr62Zo2STkNDL6ec8lMEQDJp25cK6tnrx6n1Oa8aHDc0dO0vNzQW5IyXEGK1lecWM2YXltONo6Khgmr4ejqRYHcyeehut0Oce+6BO++EdBqvN8iqVVdx4onfLrkflhv165rjYqr9xa2tdAcCrA4GZ9f+U9KJ3/g+sp0N5LqamDq7ndhtn7btzw7q2evHWcxz3kJx3NDQtb/c0HDal9UzXj8FnjvnuZ8A6xx1s4DoLLHEgQ4cFQ0VVMPX1wcH+XM0yrXHHsuFHR0FjpTwrW/Brl0QCBS2inHQlwp0zXEx1d7q8/Gr004re72XFZ3YbZ8msPmbeOJ5pA+8o3E8H/oMMaBlw9W2fVpBPXv9OIt5zlsojhsauvaXGxpO+5r3jJcQ4kQhxGuBViHE5UWPfwaCjjpZYESjUS05KhoqqIavpYEA3YEAp4XDz3KkhDe8AV7wAnjZy4jHt2MYacd8qUDXHBdb7fNdZG9FR1x/I0aDl9RyL8kVAiMkMPwexPU32vZoFfXs9eMs5jlvoThuaOjaX25oOO3L7IzXWgobZbcBlxY9PwW83VEnC4zwzMGBbhwVDRVUw9cnjjnmsK+dwuEweDxw+eVw+eXkclPs2PFPeDwhzjjj7iPuaFTxpQJdc1yMteel5P5YjNXBIL0NDbZ0/PunyLcEkEKAzAECGfTg3z9t26NV1LPXj7OY57yF4rihoWt/uaHhtC+zTbLvkFK+BbhESvmWosf7pJT3OepkgZFOlz/rspAcFQ0VVMuXEOLQtVtzOdnsGKHQ8YRCJ5Q86FLxpQJdc1yMtX9l/37ev3MnPx0bs62TXdGMSOURwosQAQRePKkc2RVNtj1aRT17/TiLfc5bCI4bGrr2lxsaTvuyupzElUKI78x9OOqkBIQQlwohbo5EIqY72s/dVTybzZruaF+8q7jH47G0o33xTumZTMZ0R/u5O6XHYjHTHe2La/L5fJZ2tC+uaXZH9vl2tJ9bUzKZNN3Rvvg1fD7fvDva75qYIJ5IHFZT4E9/YvJrX4ODBzlw4ACh0LG0t9/ACSd8q2xN2WzWdEf74pqklJZ2tC+uqbiP56upOCfAdEf74ppyuZylHe3n9nG5sWc1J7OaZrM3ez8V1wSUHXsvCYdZ5vGwzO8/rKbiPi5XU/p9G/FkDUQiizeao6E/iTeWR374g5ZqMgzD1hwxNDSE1+u1PEfM5lRci1lN8XicZDJpOyfDMCzPEWZjr1xNsVjM1hwRjUbJZDK2x57H4zF9PxXXNPv+slNTPp+3PEfM1pTP5229n2Z9WZ0jstks0WjUdk7pdNryHAGFv3V254i5fWylpng8bmvszY59O/PerC878142mzV9PxXXlE6nbb+fotGorTnCFOV2zy5+AK8tevwDhQvrv2qF68Rj3bp1tncGt7tj+fT0tG0NNzgqGiq71Tvt61WPPSZf+re/yX3J5KHnshs2SLlunZRbtlTNlxu1q3DcGl8LPe4Nw5CGYSjrRG+9Rsae1ybT7R6ZbfTIqctOt+yrnv3Wqmvo2l86zHkLxamP+61V11DhAA/LMsc0Vleu/2nxz0KIHwG/t8JdLMjlclpyVDRU4KSvRD6PMfP/3oYG2LIFNm/Gs307NDRANouUBoaRxusNOepLBbrmuBhrL7UkiB2dlg1Xw4arObj/GQ48+FpSK/2cnj2I399u26cV1LPXj7MY57yF5rihoWt/uaHhtC/VBVTXACudNLLQaCi6EFgnjoqGCpz01ej18ovTTiOWy+G96y7YuLGwdERvb2FhzA98gHRuiCdW/idtbedw3HFfdMyXCnTNcTHXnsjn+cPBg1zQ3k7Q67WtE1qynOAZFxJEks9PV+3Aq569fpzFOOctNMcNDV37yw0Np31ZXUB1SggRm/0X+CXwMUedLDDiZbY6WWiOioYKquGrxeeDzZsLB13hMIZhQDgMgQDeG74BgMcz/6okbtSva46LufYP7NrFNXv28MeZ27BV+uvYY7/AccdtJuhdZtujHZ1qc4627CvlLOY5b6E4bmjo2l9uaDjty+pXjc3mrRY3WltbteSoaKjASV+GlM+u6dTfDz4fjIzgbZ85a9HYiH/wIGee+QT5/PwD2o36dc1xMdd+wUzWLV6vkk5ra2th66BPfhLuvRfuuAPa2uxataRTbc7Rln2lnMU45y00xw0NXfvLDQ2nfVneJFsIcZkQ4kszj0scdaEBJiYmtOSoaKjAKV+pfJ7zHn2Udz/9dGGroNWrYXQUIhHy0zPrMSUSsGoVXm+IQGCJo75UoGuOi7n213V1cfPatbxwZsJS6i8hkJOT5KciJP/8M9s+LetUmXO0ZV8pZ7HNeTpw3NDQtb/c0HDal9WvGj8PvB/YPvN4vxDiOkedLDCWLl2qJUdFQwVO+dqZTDKdzzOZyxXOem3aBC0tEA7ja2uDeBwymcLzVfClAl1zXMy1z73IXrW/Jv95Hduu9bD/hEds8e3qVJNztGVfKWexzXk6cNzQ0LW/3NBw2pfVM16vBC6QUn5HSvkd4CLgYkedLDBm1+3QjaOioQKnfJ3W1MTvzjiDT69aVXhi/Xq4+WY49VRy4+PQ08PU59/GEyu/x+jojx33pQJdc6yF2n80MsLpDz3Eynvv5bxHH2WLxU+OsxpNZ16O6FlGQ8OKkptvV4p69vpxFtucpwPHDQ1d+8sNDad92bmrsQ2IzPzfnS/hXURvb6+WHBUNFTjpq93vp93vf/aJ9eth/fpDg21y4HqSI8+Qy5nvf+VG/brmuNhr/9X4OG996imklBwfCjGUTrNx505uAtabbDo7q+H3t3H66VsQwgOGAfPsB6mCevb6cRbjnLfQHDc0dO0vNzSc9mX1jNd1wCNCiO8JIb4P/BW41lEnCwxdP80s6k9/v/41XH01PPXUYZzlyzeydu1/0tlpftK0Vj4B6fApyykNq5wb9u+n2eOhzecjn8sR9noJCMHmgQFbGqJ/D7zjHfCRj9j2akenWpyjMftKOIt6zlsgjhsauvaXGxoLcsZLSvkjIcQ9wPMAAXxMSmlhXfzyEEJ4gYeBQSnlgl+sr+unmcX06W93Msln9+7lnLY23rxsGfz4x/DEE/CCF8DatYc4Hk8Dzc1nVsWXCnTNcbHX3p9K0dfQcNj1Xo0eD3tSKXsabW3IR/5G3peF+AS+8Pxny+ygnr1+nMU05+nCcUND1/5yQ8PVM15CiFWz/5dSDkkpfyELG2cPz/xeCCFWKGq/H3hSkes4LO2vtAAcFQ0VOOHr0elpHpueZkciUXjiuuvgbW+D889X0lDluKHhRo6LvfbVwSAJo7CHQXZm5eeEYbAqOP/abUdodHQw+JHjeew6QTR1v22/lnWqxDkas6+Es5jmPF04bmjo2l9uaDjty+yM12YhhAe4g8LXi2NAEDgeOBc4H/gUsN+O6MzB2sXA54AP2fRcFXR3d2vJUdFQgRO+LmxvZ1kgQPPM2k309sI733kYZ2zs58Tjj7FkyatpajrdcV8q0DXHxV77pr4+Nu7cCfk8XiEYSqVo8vnY1NdnWyNwzuUERn9E4YS7c6hnrx9nMc15unDc0NC1v9zQcNqXMLtTSAhxMoWNsV8M9AAJCmeq7gR+IqU0/97gyNf8CYXrxpqBj5T6qlEIcSVwJcDSpUvX3XrrrbY0pqenaWpqstw+l8vh89nbQckNjoqG3drd8lXgfBN4HPgX4GzHddyoXYXj1vjSbdw/ANwK7JASIQSvAd6upJGncIJegJQlL7KvZ69X9qqc2pvzqsupj3s9x/255577VynlWSV/WW737Go9gEuAb8z8/xzgV2acdevW2d4Z3O6O5el02raGGxwVDZXd6h31dccdUr773VI+9NARnOnpJ+Xw8C0ynR6rii83alfhuDW+dB33d42Oyuv27JEHUil1jV//Wso3vEHKO+8syalnv7XqGrr214LPeQvIqY/7rVXXUOEAD8syxzSWV653EC8GLhNC7KHwYfg8IcR/L4CPw5CYvS5JM46Khgoq9fVQLMb1AwP8bWoKfvELeOABGBk5ghMOn8jSpW80XbFe1ZcKdM2xVmoHeL7fz78ecww9FjebLakxPY18ZifpP9xuW9+WjsOcoz37Wp3zdOK4oaFrf7mh4bQve+fOHICU8uPAxwGEEOdQ+KrxTW77mItAIKAlR0VDBZX6+lM0yo9GRmj2ennuDTfAli2HLqpX1VDluKHhRo61Unspjpz56tGORu6cF/BMPEf85O2ckU/g9Tba9mFFx2lOPfvanPN04rihoWt/uaHhtC/XD7x0RT6f15KjoqGCSn1d1NFBs9fLS1pbIRyGDRuO4ExM3EE8HqCt7Vz8/o6q+FKBrjnWSu3FnCficb45OMhlS5ZwYUf5MVBKw9e9El70fBplnmx2DK/3GNs+rOg4zalnX5tznk4cNzR07S83NJz2ZenASwjxBynl+WbP2YWU8h7gnkpewylIhe1I3OCoaKigUl8nh8OcHArNu7L4xMSPyOUO0Nh4ouUDLzfq1zXHWqm9mPN0IsH9sRhxw5j3wKucxpo1X8fjCRQusHcA9ez14yyWOfCPJEMAACAASURBVE8njhsauvaXGxpO+zJbxysohOgAlggh2oUQHTOPVYA7q9y5BH/xFjcacVQ0VOCIr1/+Eq64Au6554hfSSlZsmQDnZ2vJBRaWzVfKtA1x1qpvZhzcWcnG5cv58vHH6+k4cEHX/oSXH45JJO2fVjVcZJTz76G5zxNOG5o6Npfbmg47cvs4vp3UFi/68SZf2cfdwBfd9TJAiOpMIm7wVHRUEElvu6LRrlrYoLU734Hu3ZBPH5EeyEEjY3rWb3603g81r/hdqN+XXOsldqLOQGPh3/u6aHV5NbsshoeD2zfjrGvn8z//sK2D8s6DnLq2dfenKcbxw0NXfvLDQ2nfc07+0kpvwJ8RQjxXinl1xxV1gzNzc1aclQ0VFCJr1tHR7kvGuW6f/s3Lti2DV760sPaTkxsYWBgM8lkP6HQavr6NtHZub4qvlSga461Uns5jpSSWD5f8iBsPo3om89i/9iDBI/7G8dx5LWElfpymlPPvvbmPN04bmjo2l9uaDjty9JyElLKrwkhXiSEeKMQ4s2zD0edLDAOHjyoJUdFQwWV+HpJaysvbWvjjI4OuOACKNoSZmJiCzt3biSZfAYpA6TTQ+zcuZGJiS1V8aUCXXOsldpLcfalUrx5xw4+uGtXyesn5tMIvvBy0se1IDy+iq+9qGevH2cxzHm6cdzQ0LW/3NBw2pfVi+t/ABwHPEphCWkACfyXo24WEF1dXVpyVDRUUImvN7S384bOTpjdKqgIAwObEcJPJjMCSAKBE8jnC89bOevlRv265lgrtZfiLPH7GUyn8QnBWDZL95zbtefTaGhYxhln/AGvN2Tbh5mvanDq2dfenKcbxw0NXfvLDQ2nfVldQPUs4MVSyndLKd8783ifo04WGLpugLooNoy980645JLCwqlzkEr1I0QQn68FIUKAF4+nkVRqT1V8qUDXHGul9lKcRq+Xm9as4ZennXbEQZcVDe/gGHziE/C5z9n2YkfHCU49+xqc8zTjuKGha3+5oeG0L6sHXo8DyxxV1gw9PT1aclQ0VKDq629TU0T//Gfk2Fjhwuc5CAZXI2WaQKCXUGgVAIaRIBhcVRVfKtA1x1qpvRzn5HCYhhJjxpKGzwe//S35LXdgpKZs+7Gs4wCnnn1tzXk6ctzQ0LW/3NBw2pfVA68lwHYhxG+EEL+YfTjqZIFx4MABLTkqGipQ9fXve/Zw/utfz76vfAVe/vIj2vX1bULKDPl8nEym8K+UGfr6NlXFlwp0zbFWajfj5KXkgVjMnkZvL8PvWM3jn0wRTf7Vth/LOg5w6tnX1pynI8cNDV37yw0Np31Zva//3x1V1RC9vfaXJXODo6KhAhVfGcNgbWMjXiHoO+uskoundnS8ghUrPszY2O2k03tpaFhl665GN+rXNcdaqX0+jiElb3rySXYmEvzgpJM4KRy2rnHpJcjhCXI59Qtf69nrx9F5zqu2hirHDQ1d+8sNDad9Wb2r8Y/AHsA/8/+HgL856qQEhBCXCiFujkQiJBIJpqamiMViJJNJIpEI2WyW0dFRpJQMDQ0Bzx6ZZrNZpJSMjo6SzWaJRCIkk0lisRhTU1MkEgkmJyfJZDKMj48zODh46Hvc2deY/XdkZIRcLsfExASpVIpoNEo8Hqe/v59oNEoqlWJiYoJcLsfIzMbQc19jeHgYwzB46qmnyGQyTE5OWqppaGjo0GsMDQ1ZqskwDMbHxzEMw3JNu3btIh6PE4/HLdU0NDREZP9+vrB0Kf+xbBm5bLZkTcPDDzM09G0Mo4mVK+9l6dJb6Oxcb7mmPXv2HJaTWU1SyiNyMqupuI9ncxofH583p4GBgbJjr1RNe/fuLTv2ytU0NDRUduxZzcmspmeeecbS+6m4poGBAdP309yaivu4uKZMOs3pfj+9Ph9j09OHanr66adNa+rsfB3Ll/+Q1taLD417O3PErCerc8RsTcW1mM0R8XicXbt22c5p3759lucIs7FXrqannnrK1hwRjUbp7++3PfYGBwdN30/FNW3fvt12Tfl83vIcMVvTvn37bL2fnnjiCVtzRDabZceOHbZz2r17t+U5Agp/6+zOEbO12Jn3du7caWvs7dy509YcUezLzryXzWZN30/FNe3evdv2+2nHjh225ghTSClNH8DbKRxsPTPz8xrgD1a4TjzWrVsn7WLr1q222huGYVvDDY6Kht3aVXQMw5DyjjukfMlLpPzBD8q2i0Yfko8//jq5e/dVrvSXG7WrcNwaX4tx3CdyOZmf83vLGlu3Svmud0n5pz/Vs1+E2Tul4dqcV2UNFU593G+tuoYKB3hYljmmsXqN13uAFwOxmYO1nUC3Re6iwNjYmJYcFQ0VqPia3LYNmUxCW1vZdi0tZ3HKKbezatWnXOtjNzTcyLFWajfjhLxePHO+prassXs38sEHyd15u21PtnQq4NSzr505r9oaqhw3NHTtLzc0nPZl9RqvtJQyI2YmRyGEj8I6XjWD9vZ2LTkqGiqwq9PQ0sIFF13ECc95Dt895xzMdrISwutaH7uh4UaOtVK7VU4yn+dn4+O8pLWVHosauQtewsDU14g996+Qv6Qqvirl1LOvjTlP1/5Sga451krtZrB6xuuPQohPACEhxAXA7cAvHXWywJiasn9LuhscFQ0V2NV55uBBlvj9iNWr8Tc2lmyTz6cwjLSyhirHDQ03cqyV2q1yvj44yA0DA3xveNiyhq/vBPLeHMe8/wGef8VFTJ3dTuy2Tzvqq1JOPfvamPN07S8V6JpjrdRuBqsHXv8KjAHbKGycfSfwb446WWCEQvZXwXaDo6KhAss6W7bAOedw5tlnc+cHPsB/7N5dtunBg7/lkUdexv79N9nTUPFVAXTNsVZqt8q5YulSTm9q4vz2dssasds+zYrrniQQ8ZBr9uEdTRD40GcsH3zVs9ePo92cp9jeTY4bGrr2lxsaTvuyeuAVAr4jpXy9lPJ1wHdmnqsZzN4ZoRtHRUMFlnS2bIGNG2HXLsTEBGzfTvB97ys8XwLp9AEgj9/fYV1DxVeF0DXHWqndKmd5QwOv7+riqv5+TnzkEc579FG2TEzMyxHX34jh9+DNQMNoHhnyYfg9iOtvdMxXpZx69ot4zqugvZscNzR07S83NJz2ZfXA6w8cfqAVAn7vqJMFhiixBpUOHBUNFVjS2bwZAgGk11tYs6u5GQKBwvMlsHz5OznjjK10dl5qXUPFV4XQNcdaqd0qZ8vEBBt37mQonabD62UonWbjzp3zHnz5908hgz6803m8KQNPIocMevHvn3bMV6WcevaLeM6roL2bHDc0dO0vNzSc9mX14vqglPLQTCalnBZClL6wZ5HCW2KDZx04KhoqsKTT30+0pYUDQpDx+/F6PPQFArTu2VOW4vM12dNQ8VUhdM2xVmq3ytk8MEBACIIeD+PZLB4haPR42DwwwPrOzpKc7IpmvKMJsu1epDQwQgaeFGRXNBF0yFelnHr2i3jOq6C9mxw3NHTtLzc0nPZl9YxXXAjx3NkfhBDrgKSjThYYmUxGS46Khgqs6IyvWMFYNErWMMh5veSEYCwaZXz58iPaFpYxsa/hBMcNDTdyrJXarXL6UykaPR6yUjKRzTKRzRL0eNiTSpXlyA9/EE/WQApJPigRKaPw84c/6JivSjn17BfvnFdJezc5bmjo2l9uaDjty+qB1/uB24UQfxJC/Am4DdjoqJMFRmOZO/MWmqOioQIrOl+77DLCySRN6TRBIWhOpQhks2zesOGItgMD17N9+5uIxR6wpaHiq1LommOt1G6VszoYJGEYBD0eugIBjgkGSRkGq4Llz121bLiazA1Xke8O45vyYHS3kLnhKlo2XO2Yr0o59ewX75xXSXs3OW5o6Npfbmg47cv0wEsI4QECwInAu4B3AydJKdV3ptUQsTmb9OrCUdFQgRWd5OQkmUAAbz5PayxGpKuLL370o9x+1llHtJ2efpREYgdCPLvCl1t97IaGGznWSu1WOZv6+shISTyfp43CPo4ZKdnU1zcvr2XD1TQ/eJAHf/Abmv/h32m57VGw+Am1nr1+HJ3mvErau8lxQ0PX/nJDw2lfptd4SSkNIcT1UsoXAo87qq4ROjo6tOSoaKjAik5nQwMjXV18c+NGtp1+OgDxfJ5VDQ1HtF279j+Ixx8nHD7VloaKr0qha461UrtVzvrOTm6icK1XfzLJ6oYGNvX1cb7FxQul1wsPPYSxdzeZu28jeNE/OuKrUk49+8U751XS3k2OGxq69pcbGk77svpV42+FEK8Vbt1usgAYHR3VkqOioQIrOqe/5z284Utf4ufHHUcklSKez5c9I+H1hmhpeR4eT8CWhoqvSqFrjrVSux3O+s5O7j7zTP6yejV3nX46f52a4lWPP04ynzcnC8HU2y/gyX/Ns+eYrY76qoRTz37xznmVtHeT44aGrv3lhobTvqweeH2Iwmr1GSFETAgxJYSo+jlBIcSlQoibI5GI6Y72c3cVz2azpjvaF+8q3t3dbWlH++Kd0pubm013tJ+7U7rP5zPd0b64pmXLllna0b64ptkd2efb0X5uTaFQyHRH+zPSac7q7iYLxIWgUwi+dvzxPE9KSzUtXbrUdEf7uTW1tLSY7mhfXJOU0tKO9sU5FfdxuR3t59bU2dlpuqN9cU2tra2WdrQvrmnZsmVlx55ZTaXGXqmaGhsbLb2fimvq7Ow0fT/Nram4j63U5Pf7Efk82w4eZDyb5a7+ftOaDMPAOPMiUsd2IDztjI8Pm9a0dOlSy3PEbE3FtVipKRQK2c6po6PD8hxh5/1UXJPP57M1R0SjUZqbm22Pve7ubtP3U3FNszfm2Kkpn89bniNma2pra7P1fjIMw9Yckc1m8Xg8tnMKh8OW5wgo/K2zO0fM1mJn3gsEArbGXiAQsDVHFPuyM+/NrrFldd4Lh8O2308ej8fWHGGKcrtn6/RYt26d7Z3B7e5YPjg4aFvDDY6Khspu9fPqDAxI+ec/S5nPy2g2K7dGInLbvn1lm+/efZXcs+dzMp0es66h4qsEHK/dIY5b46vWxv2T09NyTzJpiTNbey430z6ddtyXCqee/SKc8xxo7xanPudtrbqGCgd4WJY5prF0xksU8CYhxFUzP/cJIc62wl0s6O3t1ZKjoqGCeXVuuQXe9z741rdo8fk4p72dU8tc8JzPp4hEfsP4+M/xeA6/G82tPnZDw40ca6V2Fc5s+xPDYY6Z567GUvAaXrj6arjkEkgkHPWlwqlnvwjnPAfau8lxQ0PX/nJDw2lfVr9q/AbwQuCNMz9PA1931MkCY/bUom4cFQ0VzKuzahX09MArXmHa3uPxs3btzaxc+YnDFk811VDx5RB0zbFWalfhlGq/O5kkX2J9uCPg98P+/eQnhkj8+ceO+lLh1LNfhHOeA+3d5LihoWt/uaHhtC+rB17Pl1K+B0gBSCkPUlhiomag66cZLT79bdgAd9zBl/x+vrp/P2OZTNn2QnhpajqDrq5X29NQ8eUQdM2xVmpX4cxtf8PAAG944gl+E4lY4sfeeT6Pf1awd8UfHPWlwqlnvwjnPAfau8lxQ0PX/nJDY6HOeGWFEF5AAgghugDDUScLjNmLDnXjqGiowEwnC/xsfJz/Gh7GI4S2/aUCXXOsldpVOHPbHx8KEZjZSsgKms56PZ4VqwmHT8Mwyq/pVc9eP44uc16l7d3kuKGha3+5oeG0L6t7NX4V+BnQLYT4HPA64N8cdbLA6CyzF9xCc1Q0VFBSZ9s22LMHLrwQTyDADccfz9OJBJ1+P7kS7Q0jx759n6ep6XQ6Oy89YmNRt/rYDQ03cqyV2lU4c9uv7+jgxa2tdPr9ZRiHw+MJcOqpP0MID0Sj0Fr6BH09e/04CzrnOdjeTY4bGrr2lxsaTvuydMZLSnkL8FHgOmAIeLWU8nZHnSwwotGolhwVDRWU1Pnud+Gaa+BHP8IrBM9vaeEfly0r2z6ZfIrx8Z8zPPy9kru5u9XHbmi4kWOt1K7Cmdve7/FYPuiahcjmYOPGwkX2U1OO+FLh1LNfRHOeg+3d5LihoWt/uaHhtK95z3gJIYLAO4HjgW3A/5NS5hx1oAnC4bCWHBUNFZTUefnLYXISLrvMUnu/v5u+vg8hROmzC271sRsabuRYK7WrcMq1l1LyyPQ0ASE4tampZJtDCASQuRz5bJTUQ7fRdN7bKvalwqlnv4jmPAfbu8lxQ0PX/nJDw2lfZme8vg+cReGgaz3wJUfVNUI6ndaSo6KhgpI6r3wlfOc7JFpb+cK+fdxz8OC87QOBLpYufSPd3a+zrqHiy2HommOt1K7CKdf+zkiEK596ii/v32/pdWLvOY/HPu9l79LfHlqYsxJfKpx69otoznOwvZscNzR07S83NJz2ZXaN18lSytMAhBDfBh50VF0j+HxWL3dzl6OioYL5dB6dnub20VG2x+OcM7Nvnq79pQJdc6yV2lU45dqf29bGfzQ0cHZLC3kp8ZrsYtZ8ymsIPfVr2tpehpS5wzZtV/Glwqlnv/jmPCfau8lxQ0PX/nJDw2lfZq926BYiKWWuhrdqrKMYW7fC3r3w6ldDWxvHBIO8a/lylsxzjU06fYBo9D5aWp5HMHiMi2brOJrQ6PXyP6eeisfiXOTx+DnppO+DlLB/P5RZ+LeOOuqowy2YHXidUbQnowBCMz8LQEopW6rqzkXkcvYvXXODo6KhgkM6UsK3vw07dsCSJXDJJSxvaOBfenrm9RWN3se+fZ+no+MVHHvs5+bXUPFVReiaY63UrsKZr73Vg65DyGTgn/8Z+vthyxZoa1P2pcKpZ6/5nFel9m5y3NDQtb/c0HDa17zXeEkpvVLKlplHs5TSV/T/mjnoAmhoaNCSo6KhgsN03v1uuPDCwsNKeyAYXElHxytobX2pZY5tX1WCrjnWSu0qHLP2hpT84eBBvmblWq9AALmkg0xDiugjt1TkS4VTz34RzHlVaO8mxw0NXfvLDQ2nfVldQHVBIIS4VAhxcyQSMd3Rfu6u4tls1nRH++JdxaempiztaF+8U/r4+LjpjvZzd0ofHBw03dG+uKZ4PG5pR/vimmZ3ZJ9vR/u5NY2MjBR2f08kiJ5yCqmrr2ZiaorHYjH+e9cuxjOZw14jHo8ftvt7MHgmHR2bCIX+rmxN09PTpjvaz61pYmLCdEf74pqklJZ2tC/OqbiPy+1oP7emaDRquqN9cU2RSMTSjvZz+7jc2DOrqdTYK1XT6OiopfdTcU3RaNT0/TS3puI+tlLT0NDQvDU9deAAV/X38+39+3kyFjs07suNveh7L+Rvn8kz0PV7Bgf3H6ppenra8hwxW1NxLVZqGhkZsZ3T5OSk5TnCzvupuKbBwUFbc0Q0GmV8fNz22JuamjJ9PxXXtG/fPts15fN5y3PEbE0HDx609X7au3evrTkim80yODhoO6exsTHLcwQU/tbZnSNma7Ez7w0PD9sae8PDw7bmiGJfdua97MyiylbnvbGxMdvvp8HBQVtzhCnK7Z6t02PdunV2NgWXUtrfsTybzdrWcIOjoqGyW305nWv37JHrHnpI/ueBAxX7coPjZO1Octzqr6Np3H/7wAH545ERmc7npZTz124Yeblz5wfk6OjPZD7/7GvrUosTnKMp+7lw432va3/V57ytVddQ4QAPyzLHNFqf8XITExMTWnJUNFQwMTEBP/wh3HADFG0IekZTEy9pbeWFLS1Htp9BMrmbeHw7UubNNVR8VRm65lgrtatwrLR/a08Pr+/uJuAxn8aE8HD88TfS1X4JnieeVPalwqlnr/GcV8X2bnLc0NC1v9zQcNqXO/ftLgIsXbpUS46KhgqWLlkCP/gBjI3BS14CM5uCvrKzk1eW2C6h2NfIyC2Mj99BX98HWbr0H8pruNTHbmi4kWOt1K7Csdtellij6whkMnD55TA6Cr/+NXR1aVmLKscNDV37SwW65lgr2R/NtZuhfsZrBgeKzvLoxFHRUMGBkRG48cbC3V/Pe555+yJffv8SGhpWEA6fYZlj2ZcL9euaY63UrsKx0/7fdu+m+7772ACc9+ijbCn36TQQwDjxOFLdEHnyFiVfKpx69prOeZrmWCvZH821m6F+xmsGvTNneHTjqGiooLe3t3CW68QTDz33YCzG0kCAlQ0NR+y9WOxr+fJ3sXz5u6xpqPiqMnTNsVZqV+FYbb9lYoKvDw6SNAxagaF0mo07d3ITsL7EmdrpD72apwfvJdDwO9rl+7SqpVKOGxq69pcKdM2xVrI/mms3Q/2M1wx0/TTjyqc/KY/QkVLy73v28NrHH+eZZNIRXzp80nBKo/7pr7ocq+03DwzQ6fezMhikDQh7vQSEYPPAQMn2zT0vY0nXq1m58mNKvlQ49ew1nPMUdHTtLxXommOt1G6G+oHXDHT9NFP1TwBbtsCaNfSefDK86EWFn4GUYXBmUxPHhkIcGwqV9ZVOD2EYGUtSOnzScEqj/umvuhyr7ftTKVq9Xlq83kPPNXo87EmlSrYXwsOqVVfhueN3JE9upeOslUyd3U7stk877k21vSrHDQ2dsq8UuuZYK9kfzbWboX7gNQNLa28sAEdFwzK2bIH3vAcOHMBIpwsXHW/cCFu2EPJ6ufbYY/nxKaeUXCl81tczz3yERx89h3h8h6mcW33shoYbOdZK7Socq+1XB4MkDOPQz3kpGc/lWBUMluXEbvkU4Ss/S8PuafKNPryjCQIf+ozlg6969tXluFG7io6u/aUCXXOsldrNUD/wmkF3d7eWHBUNy9i8GRoaYM0axPLlha1UAoHC8xZ8GUZuZgkJSTC4yhLHLqpafwUabuRYK7WrcKy239TXR0ZK4vk8eeDpZJKRTIY3zsMXX/kqRsiDEfIgpIFs9GH4PYjrb3TUm2p7VY4bGjplXyl0zbFWsj+aazdD/cBrBpFIREuOioZl9PdDYyP4/eSamgrPNTYi+/vZEY9jzHOLfiQSwePxccopt3LGGb/H6y1/hqGYYxdVrb8CDTdyrJXa/3975x0fV3Xm7+dMV2+2LMuSsQ3CpjsxMeymgUM2MSSbvmxCQhJC2CWUhGLyYwOsgZRlvUCKSTaGBJbQ00mCQwo23cYGGzC4YhtLVrNGZfrMnXvP74+5I8ayRjNzNXN1Gd3HH31kjc473/d73nvPHN12jMTk2355UxOrOzqY7fUSBlo8Hs6sr+cj41xYn8bdFSQ+y0WsBVR36tlz0ufE3RUqam5G2xuNMUPDSrWfLFatY7nUfjp7z4XpEy8hRLsQYp0QYrsQ4jUhxNfNzmE8asc8INQqMUY08mbOHIhEAHCmr5GJRIjMncvnt2/n89u3Zw3NzMvprMpLzqw+NkPDjDqWi3cjMYW0X97UxBOLF/MgsH3pUv508sm0TLC2mtJWgyMOQjgRwgOAiKkobdVFz81Ie6MxZmhYrfaTwap1LJfaT2fvuZiKI15J4Cop5XHA6cAlQojjpyCPw4joExCrxRjRyItgEAIBOHgQQiE0VYVwGBIJ3rzsMmZ7vSyqrJwwL01TCpI0q4/N0DCjjuXi3UiMUe8eh+OwaxIDyeQRbeRVV+BQNBxRgUhK3L1xHAkNedUVJcnNrr1FxrxJ6li1v4xg1TqWi/dcmP4cLyllD9Cj/z8ohNgOzAFeNzuXTDwejyVjjGjkxa5d0NAAbW0Qi+HYuxcWLIAVKzh++XL+ACQyLloei8sFL7+8jIqKDo49dg0OR+5Nyaw+NkPDjDqWi3cjMZP1HlVV/ruzk02BAA8dfzzVrre2z9pzbyAAiFtvp+K1EQQOkh9bRu25N5QkN7v2FhnzJqlj1f4yglXrWC7eczGlD1AVQswD3gFsnMo8AFR14nUGpyrGiEZeLFkCf/hD6qhXWxvhYJCamprDmky0Bl4ksgdNi6Np0bwmXWBeH5uhYUYdy8W7kZjJencLwd5olMFkkm3hMKfX1R32+9pzb4BzbyC04TnUn11N10cGOVYZxO1uLHpudu0tMuZNUseq/WUEq9axXLznQuS1xlkJEEJUA08C35FS/mac318EXAQwa9asJQ899FBB7x8Khaiuzu+aDQBN03Dksdiu2TFGNAr1nqkTBdzknpGn2seBEaClIA0jeeXLZLyXMsas7cve7t/y3g+owOycOj8BtgEXAkuKnptde2uPeaXMyx7zpu92f+aZZ74opTx13F9KKU3/IvXZ/jhwZT7tlyxZIgtl3bp1BbWPRCIFa5gRY0RjQu9+v5Tr1kmpaePq/PTgQfnuF1+UD/f1FT0vM2IKrbsRDSMxZvWXvd0XRiQSkdHoARmLdaVeGLNfZIspVMNIXoVi174wrFpHe8wrDKtu98BmmWVOMxV3NQrgZ8B2KeVtZutnIzrOsjhWiDGiMSH33gtXX51aEHscne54nJim0ex2Fz0vs2LM0DCjjuXi3UhMsb1vCgS4af/+Ix6REo1G8fna8Q454frr4ZZbip6bXfspHvOKpGPV/jKCVetYLt5zMRV3Nb4b+AKwTAixVf86ewryOIyx1zdZJcaIxoTMnQszZsDZh3d5Wmfl/Pk8fsopnJ7l9lm/fy0vvng627bNZePG4/D71+YtbVYfm6FhRh3LxbuRmGJ6j6oq/7FvH48ODPDXoaHxdeJxePxx1N8/QvzQrqLmZtd+ise8IulYtb+MYNU6lov3XJg+8ZJSPiOlFFLKk6WUi/Wvx8zOYyxDYwZkq8QY0ZiQT34S/vhHWLQoq06T240vY+27NH7/WnbvvpR4vBNNU1GUQ+zefWneky+z+tgMDTPqWC7ejcQU03uF08m1c+fyb62tnNXQML7OUUcxfOn72HZDku7QfUXNza79FI95RdKxan8Zwap1LBfvubCfXK8zc+ZMS8YY0ciJ68hL52fOnDnhk+oBOjtXIYQHj6eFysr5eDwtCOGhszP3EkNpjUIpif8iaJhRx3LxbiSm2N6XNTTw1dZWnGPWHc3UA7CNDAAAIABJREFUqTj3SuTsJjyelvS1qEXJza69Bca8IuhYtb+MYNU6lov3XNgTLx2rLoBatMU577oLHn0UxnmgZFrnwp07uXDHDrrj8XHbxGL7cDgqAYGqunE4fDgclcRi+/NKwQqLkxZLw14wtrQxpfQeU1WeHh4+QsfrbeWkk9YyZ87XEJ2dRcvNrr11aj8ZHav2lxGsWsdy8Z6LKX2Ol5WYPXuim86nLsaIxhH09qYmXpoGJ58M8+Yd0aSuuZnXu7sRQtA4zhExAJ9vPvF4D05nFW794ntNi+S1QDaY18dmaJhRx3LxbiSmVN4TmsYXduzg5WCQerebQ4rC/L4+VrS3s7ypCafDC9dcA088AfffDwsXTjo3u/bWqP1kdazaX0awah3LxXsu7CNeOt3d3ZaMMaJxBM3NsHIlfOlL4066AIb7+vjbKafwk46Oca/vAmhru5p4vJNY7E0SiRiqGkbKBO3tK/JKw6w+NkPDjDqWi3cjMaXy7nE4aHK56E4k8CcS1GgaPfE4l+7ezVq/H4RAzppF0pPk0AvfL0pudu2tUfvJ6li1v4xg1TqWi/dc2Ee8dFpbWy0ZY0TjCBwO+PCH89JZPMHdGz5fO273bFR1CCHCeL3zaW9fQVPT8rzSMKuPzdAwo47l4t1ITCm974xEaPN6qdb/wPAAqCqrOjtZ3tSE+uVzef3UX5Go2kRF6FWqq0+aVG527a1T+8noWLW/jGDVOpaL91zYR7x0enp6LBljROMwslzTZUSnuvokFi9+nJNO+iPz5j3H4sVP5D3pylejGDFmaJhRx3LxbiSmlN73x+NU6U+hVhSFuKZR6XCwPxYDwNXYRvPCi2lru5yKio5J52bX3jq1n4yOVfvLCFatY7l4z4U98dJpaclv2RuzY4xojLJ/f+p5XQ88MGGzQ4kEVw8NsSaPw6kVFQuoqzvdsv1lBKvWsVy8G4kppff5Ph8RfQH4gBDsjcXoUxTm+XwZ+ufTMusLOJ/fDNu2TSo3u/bWqf1kdKzaX0awah3LxXsu7ImXzqFDhywZY0RjlMcfh8HB1ARsAjYFg7wWCPBaOJy1jaYpk87LrBgzNMyoY7l4NxJTSu8r2ttJSElYVZHJJKqUKFKyor398IaPPgrf+Aby1luR+kTNSG527a1T+8noWLW/jGDVOpaL91zYEy+dhjEPVrRKjBGNUS66CH74Q/jKVyZs9oGGBn587LF8OcusXkrJ669/lj17rkZRhgznZVaMGRpm1LFcvBuJKaX35U1NrO7oYLbXi3A6eUdNDfcsWsTypqbDG37wgyTaqulauI3AyLOGc7Nrb53aT0bHqv1lBKvWsVy858LSEy8hxEeFEGsGBweJRCIEg0ECgQDRaJTBwUEURaG/vx8p5eg52PTdB4qiIKWkv78fRVEYHBwkGo0SCAQIBoNEIhGGh4dJJBIMDAwwMjIy+qyO9Hukv/f19ZFMJvH7/cRiMUZGRgiHw/T39zMyMkIsFsPv95NMJunr6xv3PXp7e9E0jc7OThKJBMPDw3l5CgaDo+/R09OTlydN0xgYGECTkt4FC2DWrKyeHt63jw9t3cq5r7/O13ft4rcHDx7hqatrI/H4AQYHt+By1dLd3U0wGBz1NDAwkJenQCBwRJ1yeTp06NBhddI0bcI6SSmPqFM4HJ6wTpl9nK+noaGhrNveeJ4GBgaybnvZPAWDwazbXi5P421743nq7e3Na3/K9DQ0NJRzfxrrKbOP8/HU1dVVsCdN0/IeI5Y3NXH/rFlsPvZYft/Wxlm1tQwODtIdDPLqoUMpT0DXrf9K7wc0Og8+Muop00s+nnp6egqu0+DgYN5jRCH7U2adOjs7c+5PYz319/cXvO2NjIzkPUZIKdm3b1/BnlRVzXuMSHvy+/0F7U/pvAoZ9w4cOFBwnfr6+vIeIyD1WVfoGDG2j/PxdPDgwYK2vYMHDxY0RmTmVci4pyhK3mNEJBKhr6+v4P3pwIEDec0jMj1NSLbVs630tWTJkoJXBrfqiuVmrPD+7K9+JeXgYM52jw0MyAXPPy8XbdggF2/cKBdt2CAXPP+8fGxg4Ii28fghGQi8NKm8zIgptO5GNIzEmNVf03m7n2zt++Jx+YlXX5XLX35Z9sbjUkopVTUmDx16VKqqYjg3u/bWr30p2psVY49560quYSQG2CyzzGksfcTLTNKzZqvFFNR+7VpYtoyl558P8+fD//zPhM1XdXaS0DSCqkpU06hyOvEIwapxntrt8cygpuYdxvIyOcYMjZLWcRIxZmhYtb+MkKlT53LR4HJR73KRfpKdw+FlRuM5OP78F7jyStA0u/YljpmK2peivZkxZmhYtb/M0Ch2XvbES0eMWcPNKjF5t1+7Fi69FLq70TweiMdh9erU61nYF40SSCYZ0i8wBg67rR4gmQxNLq8piDFDo2R1nGSMGRpW7S8jZOp4HQ5uP+YY7ly4kBkez1uNVBV+8hOSv3uQyHE1eBY1ElzaQODhmwrWMJJXqbBrb819uFxqP52958KeeOk4szytfapj8m6/ahV4PFBdTby5ObXUSUVF6vUszK+oYKbHwwy3mxpdJ6Jpo7fVSynZseOL7NhxAYlE/6R8mBljhkbJ6jjJGDM0rNpfRhirU+tyUZXx2nMjI2guFyMnelGHe5GxKMlaN87+CJ4rb85r8mXX/u1R+2K3NzPGDA2r9pcZGsXOy5546SQSCUvG5N1+3z6orHzrZ5cr9fMEj5JY0d6OJHWUS9M0wqpKIuO2+nj8IIpyiHi8G7f78Du+rNpfRrBUHScZY4aGVfvLCBPp3Nvby+W7d3PLgQM4/v53krUCrcIBAmSlC83tQNx6+6Q0ihljhsZ0qX0x2psZY4aGVfvLDI1i52VPvHQqMyctForJu31ra2qSlfmk+kgk69qMqpSH3VY/IiWzvV5Wd3SM3lbv87Vx8smPc8wx30eIw2f8Vu0vI1iqjpOMMUPDqv1lhIl0Tq6qosLpZFFlJe6uIPi8OBxehCYQSYn0OXF3jX8qPl+NYsaYoTFdal+M9mbGmKFh1f4yQ6PYedkTL51AIGDJmLzbz54NoRB0dYGUEA5DIgErxl/A+vp9+7h8926Oq6zkicWL2dTRwROLFx/xLCOns4KqqkWT9mFmjBkaJavjJGPM0LBqfxlhIp3FNTU8euKJfGLmTJS2GmIxF33ROrQuiB1yEos5UdqqJ6VRzBgzNKZL7YvR3swYMzSs2l9maBQ7L3vipdPY2GjJmLzbr1kDn/40HH88rmAwNRFbvRqWH7mWYlhVeWZkhI2BAJX6ueuxOolEP1JqR8QWnNcUxJihUbI6TjLGDA2r9pcRcuk0uN0AvHTxVQQTlWiKREgNh6ISjlfw0sVXTVqjWDFmaEyn2k+2vZkxZmhYtb/M0Ch2XvbES6e/vz93oymIybt9YyPcfz889RQbH3wQnnhi3EkXQJXTyaMnnsh/LVhAs373VqaOlJLduy9l27ZPEosdKIoPM2PM0ChZHScZY4aGVfvLCPnqXLf4HL7xjSvomjmLQHUVOzrauf6KC1i5+OyiaUw2xgyN6Vh7o+3NjDFDw6r9ZYZGsfNyFfXd3sZYdQHUCdsHAvDMM6kJVoG3u9a73ZyZsQxCpo6iDKCqYaRU8HhaC88rC1ZYnLRYGvaCsaWNsdpCyV2xGG+8+7186h/fw1E+H0mlH6ejlpF47otu7dq/vWtvtL2ZMWZoWLW/zNCwF8kuEeklAqwWk7W9lPCd78ANN8Cdd+b9foP6UkoT6Xg8MznppD+wcOFdOBzjz82t2l9GmNI6FjnGDA2r9pcR8tWZX1FBncvFUT4fqqLgcTfjiChcd++98LvfFUVjsjFmaEzH2httb2aMGRpW7S8zNIqdlz3x0mltHf/IzlTHZG0vBLz//TBzJpxzTl7vpUnJl3fs4PwdO+gfc3vsWB0hHPh8cwvPawLMijFDo2h1LHKMGRpW7S8j5Kuzor2dhJREVRWXy0VYVZn38st88OmnSf7wFhLB8U/JF6Ix2RgzNKZj7Y22NzPGDA2r9pcZGsXOy5546Vj1r5kJ2599Nvz+9zBnTl7vdSAWI6yqBJJJZugXDY/VicUOoGm5T59Ytb+MMOV1LGKMGRpW7S8j5KuT+eiVQ/E49W43r5x2Gr9Yvox1l7t5s3fVuEeSC9GYbIwZGtOx9kbbmxljhoZV+8sMjWl1xEsI8VEhxJrBwcGcK9qPXVVc0U+pTbSifeaq4i0tLXmtaJ+5UnpdXV3OFe3HrpTu8Xhyrmif6am1tfXwldLjcQ7t2PGWJ1U9wlN6Rfaxq7/Pq6jgzhkzuP2YYzjU33+Yp6qqKkKhELt2XclLL53F0NCrE3pqbW3Na0X7TE+zZ8/OuaL92DrV19fnXNE+s05SyrxWtM/0lNnH+XqaOXNmzhXtMz01NDTktaL92D7Otu3l8jTetjeep+rq6rz2p0xPM2fOzLk/jfWU2cf5ePJ6vQV70jStoDGip6eH2bNn5z1GfKihgQdaWjjwnvfw65YWfrxwIbMuOJ/m+V6SyRaSyfi4nqqqqgqu04wZM/IeIwrZnzI9eTyenPvT2DrV1dUVvO21tLTkPUZIKUeXZynEk6qqeY8RaU+NjY0F7U9pChn3XC5XwXWqqanJe4yA1GddoWNEmkLGPZ/PV9C25/P5ChojMilk3Euvo5jvuFdTU1Pw/uRyufIaIzI9TUi21bOt9LVkyZKCVwYvdMXy3t7egjXMiDmi/W23SXnWWVJu3Jg1xshq9b29vVJRRuRrr50nt249S6pqorC88tQodYxR76WOMau/yna7z4Opqn0yGZJSStn/3HMycvBgSTTywa59YVh1H7bHvMKw6nYPbJZZ5jSWPuJlJk1jHhxqlZjD2qsqvPFG6m7GqqqC3ufVUGh0IexsOi5XLccffx/HH/8QDoc7a9sj8soTs2LM0JhUHUsYY4aGVfvLCMXw4nRWMfyrXzF84YU8fc01jIy5ftKufXnU3qr9ZQSr1rFcvOfCnnjpjIyMWDLmsPZOJ/zwh/Czn8EJJ+T9Hn2JBF/ZuZNPv/YaCW38h6Jm6oxdlzFnXnliVowZGpOqYwljzNCwan8ZoVheQqefTrC2ijfaIDD4WEk0io1de2vuw+VS++nsPRf2xEunqsAjSGbFVFVVpR4dkcbhgBNPLOg9+hIJ5ni9HFdZiccxfskdjm4UZbiwvArErBgzNAzVscQaRrBqHc3wblRnvJi2tjaqHryW4z61j+DgLw9b9cGufXnU3qr9ZQSr1rFcvOfCnnjpxONxS8bE43F48EG4/vrU+osGOLm6ml+fcALXzh3/8RBSSg4cuIlXX11OKPRK/nkViFkxZmgYqmOJNYxg1Tqa4d2oTraYY4/6OCfMu4qFC+9CKEku37mT0158kYUvvcSyrVtZ6/eXNK9CsWtvzX24XGo/nb3nwp546bhchT/Ev6Qxa9fCsmXUnHwyXHIJPPQQbN1asF4ahxDUZNHWtChu90yczhoqK49cEHs8LNdfk8CMvKazdyMxZng3qpMtRgjBzJmfxLn9DXZ8/OMc/PWveTUcps7ppCce59Ldu/OefNm1Lz1W3YfLpfbT2Xsu7ImXFVm7Fi69FHp6kI2N0NQEIyOpi+oLQJOSRwcGiKpq1jZ+/1peffUjDA8/hqbFGRr6+2Szt7GZ3hw4gNi/n0898yRz3E5cQlDldOIRglWdnVOdnY2NzRRjr9Wok0wmrROzahV4PFBVhVRVqK8Htzv1epaFr8fjqeFhbtq/n0f6+7nv+OOP+L3fv5bduy9FCA8ORz2JRD+7d18KrKapaWIdS/XXJDEjr+ns3UiMGd6N6uSMWb6c/37lBdzqEL+45ELaegbomt3MT877PH847b30JxKji9MXM69CsWtvzX24XGo/nb3nwj7ipeP1eq0Ts28fBIMQDOJIXwxfWQn79xekVeNycXJ1NedkuRW2s3MVmhZFCCdOpxOnswohPHR2rsr53pbqr0liRl7T2buRGDO8G9XJGSME9T4X197xIDP8wwzXVjHDP8R/3vpD3v/cM2wNhUqSV6HYtbfmPlwutZ/O3nNhT7x0wgYuXC9ZTEMDDAxAdzda+plAkQjMm1eQ1pKaGn6+aBHnNjeP+/tIZDuKMkw8/iaqmnr6r8NRSSy2P+d7W6q/JokZeU1n70ZizPBuVCefmG/c92Pibg9RXwUNIyGkcJB0O7np4bs4q6FhtN3WYJDgOH9N27UvPVbdh8ul9tPZey7siZdOXV2ddWK+/W2oqYHGRpxud+puxkQCVqwoWA9SF9aPR0XFQpzOStzuWbhcqQemaloEn29ezve0VH9NEjPyms7ejcSY4d2oTj4xM7v7qauIMHN4mIbhILMH/NR5g3T07B/dH0PJJFe+8QYf27aNXv2uqbV+P8u2buXUXbsKvhOyFD6mIsaqtbdqfxnBqnUsF++5sCdeOn4DA1zRY9IPNz37bLj3Xjj6aNSBAZg9G1avzvv6riHgfw8exK+vYZWNuXO/ictVhxBuFEVBVcNImaC9PfcEzxL9VSTMyGs6ezcSY4Z3ozr5xChtNdTEwtQ0xnHWaThmxKmOB0jMqRhtE1BVOioqOKaiglkeD2v9fi7dvZueeJwaKPhOyFL4mIoYq9beqv1lBKvWsVy858KeeOnMmjVramMefRS+9rXUKUVITbKeeALXgQPwxBMFXVS/Drirp4dbx7mDKpkM0N19J1KqNDUtp6NjNV7vbIQI4fXOpqMj94X1E/qwQIwZGoXGTGfvRmLM8G5UJ58YedUVOBQNEU2SbHADDhxJB/LqFfDss9DbS6vXy/8eeyy3H3MMQr/j0Qn0KgoRE+6EtGtvzX24XPb76ew9F/bES2fs6uimxsRi8NOfwubN8OSThjXSpykeAPoVhfZx7pzat+96urt/Smfn7QA0NS1n8eInmDv3GRYvfiKvSVeheZkdY4ZGoTHT2buRGDO8G9XJJ6b23BtI3HY9anMlzkACtbkG5bb/pO6dn4VrrkE7718J7f0bQp9gAeyLxYhrGglNI6Afra50ONgXjZLMstTXZLBrb819uFz2++nsPReWnngJIT4qhFgzODhIJBIhGAwSCASIRqMMDg6iKAr9/f1IKenp6QHe6iBFUZBS0t/fj6IoDA4OEo1GCQQCBINBIpEIw8PDJBIJBgYGaGlpobe397D3SH/v6+sjmUzi9/uJxWKMjIwQDoepq6tjZGSEWCyG3+8nmUzS19c37nv09vaiaRoej4dEIsHw8PBbnhIJoj/4AaGLL0Y566zDPLW2to6+R09PT1ZPv+nq4ms7d3IwGqVRSnxS8j9dXTywZ89hebhcH6WysgOn80OHeaqqqiIcDhMOh/Py1NraOuppYGDgSE/j1Gn27NlH1GkiT8FgkPr6+sPqpGnahHWSUh5Rp1yeMvs4X08zZ87Muu2N56mhoSHrtpfNU2tra9ZtL9865fJUXV2d1/6U6WnmzJk596exnjL7OB9PXq+3YE+aphU0RvT09DB79uy8x4i0p0wvE3lyfuQqHOu6iG/3436qB+WsS0g2NRFetJDBEyK82ns1fv+fR99jjsNBhdPJbJeLGR4PqqoSTiaZ4XTygS1bWP3mmwXVKZcnj8eTc38a66murq7gba+lpSXvMUJKidCvgSvEk6qqeY8RaU+NjY0F7U9pChn3XC5XwXWqqanJe4yA1GddoWNEmkLGPZ/PV9C25/P5ChojMilk3FP0P1LyHfdqamoKGiOklLhcroLGiJxIKS3/tWTJElko69atK6j9wYMHC9aYdEwwWDSNM7dskQs3bJDv3LRJHrtunVyyaZNctGGDPHPLliPaappqWMdoe7NiCq27EQ0jMWb119tiuy+Rxtul9lpSkV17fyC3bv2gjMV6pBweljISkY8NDMgFzz8vF23YIE969lm5aMMGueD55+XXd+2SSzZtkqvefHP0PZKaJvvjcSmllI8NDMgzt2yRLevWyTO3bJGPDQyUxIdZMVatvVX76+2y3ZcqxqpjHrBZZpnTWPqIl5m0traaG/PKK/DP/wx//WtRNN6IRulPJOjKWFOq0uFgfyzGyMhzhELbRl8X4siyF+rF9P4qIWbkNZ29G4kxw7tRncl6EU4Xc+Zfzgkn/AqvayZccw18+ct8MDzA6o4OZnu9hBwOZnu9rO7o4PZjjuG+447jcxnXmWwIBDjn1Vf58vbthi/It2tvzX24XPb76ew9F/bESyevw4PFjNmwIbUE0LPPFkVjjtdLXErimkZ6gaCIpjHXA3v3fpNduy4iGn1j0jpG25sZY4aGVfvLDA2r9pcRptKLy1ULfj8cOkSifzfb91zAkj9+k99fdCavf/I9/P6iM3n33+5ACMGiqipaMx7iuD8Ww0FqAubRrxPTgLiUCCnzuiDfrr019+Fy2e+ns/dc2BMvneYsDxktWoy+6PWsf/gHWLYM2trge9+D668visb1Rx1Fo8tFs9uNAwirKgkpuWbuAhobl9PQ8AF8vgXF8WKgvZkxZmhYtb/M0LBqfxlhyr00N8O999L7raU4t+6j5j/uxtkfQa114+yP4LnyZgIP33RE2HmzZvH4KacQ0TQq9dUtYkBfIkFAVdkfiwGpS0l2RSJoUo7Gpm/C+Yd9+wp+VtiU91cRseo+XC77/XT2ngt74qUzODhYupj0otednSSrq6GnBy67DJJJ0O9oMqKxKxLhuZERAJY3NbFm4ULafD6CMHqa4uwZzcydey3z5q0cvWh1Ul4MtjczxgwNq/aXGRpW7S8jWMJLdTXty+6g7cEg0u3CoQo8vQrCIdDcDsStt48bVudycXRFBRH9jkeX/prX4WCezwekjox97vXXOff11wEOe1ZYLYU/K8wS/VUkrLoPl8t+P52958KeeOnU1taWLmbVqtQE69AhXD09UFGRWgR7Ve41EbNpHIzH+erOnVz9xhvs1J/9tbypiScWL+ZBNB6es5cPNaRihRAIMfEEr1D/Je2vScaYoWHV/jJDw6r9ZQSreBHCgedgFOl14BpScMQ1pJpA9SZxdwWzxq1obychJWFVxQPUOZ14HA5WtLcD4FcUWr1eOipSD25d1dmJRwh6Egl6NI3KAp8VZpX+KgZW3YfLZb+fzt5zYU+8dCLpB5eWImbfPqiuBimR6efx5LnodTaNVo+Hf2ps5H11dczX/7r1+9eydesy4BPs2PElXnvtX/LLbwKdYrU3M8YMDav2lxkaVu0vI1jJi9JWg4hrxOd4UZpcqBUCRzz1Oj/+Mdx2G8rBnYfFLG9qGr0gP/NI9/KmJgBOra3l0ZNOYqW+zuu+WAwnoEhJXFURvHUTzhV79vDvO3dyQD9NmUn69GTH5s0Fn54sl9pbdV8xgpW2+8nGmKFR7LzsiZeOZ5yHjU4qprsbvv/91OnE+fMhGoW2NmR7OzgceS96PVYjfa2GEIJr587luwsW4HE48PvXsnv3pcTjPUADUmoEgy/g96+dvJcitDczxgwNq/aXGRpW7S8jWMnL6NPu4yrJaifOmAunIpCXXQoPPYR2/z3sfOFf2Lv3WmTGNVtvHemGJxYvHp10HZaDfh3YfJ8PFTimooJWPa+IpjHP52NzMMjmYJDajMsfVnd1cdaWLVy4cyc98TiNTifd8TiX5HF6Mj1ZW/z66wVN1qxae6vuK0aw0nY/2RgzNIqdlz3x0lFVNXejfGOkhCuvhPvuSy0FtGJFapFrTUtNugpY9DpT45H+fi7fvZuEftTMIQQOIYhEdrNnzzcQwo3TWQV4qaxciNNZS2dn7tOZE3opUnszY8zQsGp/maFh1f4ygpW8ZD7t3hVQUJsrSdx2A7VfuBnuvJPIlz9I4qhKhPCmrte84w743e8Yue86gksbOO2zHyC4tGHci/HTpE9NJjQNnxCjN+Fc3dbGb044gR91dFDvdo+2fzkc5oVQCLd+56QQgqSUdMfjXLZ792i7iKryYF8fTw8PA4dfS9bgdOZ1LVl6onb81q0lXyAcrLsPl8t+P52958JV1Hd7G5P5F6ShmMHB1OlDnw+EgAsvhKeegqVLU3cwrl4Nq1Yh9u6FBQtSk6481l9MawSSSe7q6WFQUdgQCPC++vrRNp2dq4jF9uP1tmdEChyOSmKx/YV7KUF7M2PM0LBqf5mhYdX+MoLVvNSeewOcewOBQODw60oWLqR64a2cpAwipQJ9ffB//4cy8CbeSD+az0myxoWrJ4y48mYC6fcaw/KmJn7+6E+o/NH9zOrx0ze7ichl5/H+008HYOaYv+xvPfpoHvP7aXClPiokoJL6i304mRxt1x2Pc2tnJ/N8Pt5bXz96LdlgMklS02j3+UDTWNXZyVyfjwFF4bjKSpp1vfREzSME9RkTtdV6ztlY6/ezqrOT7cBxW7eyor19wvaZWHUfLpf9fjp7z8WUHPESQnxYCLFTCLFHCPH/piKHNE/e/W02vfM4hhcew6Z3HseTd3+74JhXLz8PPvYxePjhtxqddRbcdFNq0gU8vMjB0pu+QNs9q1h60xd4eNHEXf/wvsdZ+szdLHop9X1t599Z3dHBZXV7aOy6kETiraUfZsz4OF5v2xEbh6ZF8Pnm5dUP7oy/ckvR3swYMzSs2l9maFi1v4xgVS/ZYtzuRjyeWdDYCCtXoqhDqF7QKhwgBO4hFfdAEvmdm9m/Xz/y9dJL8NRTxHu2MfLwSk677kZOGNxLU12QEwb3ctp1N2Y9SlbrcrGwspJ3Pv03Vl/0Rf7wsXN48Gtf4V83P8eJVVWj7SqcTj7T3MxZDQ1A6lqyM559gocvvYQX/+Uz/O+/fYkznnuC/bEYv+zv56o9e3g+EBiNv27fPk56aj13XvxV/vjxj3D3xRfw/mfXcfnu3dw5ZjmZl4JBnhoe5lf9/Tx0zxq+fcHneeGzn+HbF3yeh+5Zk/NIWaFjfjE+I0oRk26/4LP/Ysm8zOqvfP0b0UhT7PEvg2khAAAaJ0lEQVTIuXLlyqK+YS5E6va6PwMfAr4H/PDGG298auXKlYeyxaxZs2blRRddVJDO/v37mZfjGqon7/428771I9xxhUiFl5pghKa/beDlxjjz3vE+kkqcRDx1UZ3DmfqLb93PbmL+t1bjiScIV/moDkap37IdGQ3ibGuHM84cfWxDQlVQNIVf7v8bVx0IENOcVBInoHlZO5KgTXZxYsMxaJpGXI2T1JK4HC4e3vc4V745Qkxz4UQlrLlZO5LgFFcfp/McodAWPJ5WqqtPBKCysoOKimMZHHwMKTWSSQ2nM4mUCebP/w6VlR05+ysYDFKh3/mUD4W2Nysmn7pPRV5m9Veh/q1aRzO8G9WxRO2dTujoQF11I1q1ByGcSE3iDqUeoIqmMXT+CTQ1nQ2rViHXrGFf1cNU/vA3OBIOhHDiGkkiXSBdEu3lzXg//3VYu5ZE1+uEmoYADZerno6Hbudjt/wYb0IhVOmjJhjhA888xzsX1tB+8ulIoVHrcvOe2lpOrahAqgqdv3uAq1b9gOpEjHCFj/pgkPc98xyBebM46R2n4VZVzqiuptnjQKKx7oH7WPX9H1IRixOp8lIbDPOBZ55jY0sLr8yYyRdmNCEdGlJKrt//Jvd3daE++gjfuu0HVMTjBCqrqAlG+ODTz/JItYOzTjud7niCi3fs5JXBQc6orUYKjafu+S5zv3UH7liSSKWH6lCUpr9u4E81Cba2HI1P02h0ghQa3fEE96+5hVP/80d4M8b7hr9u5LfeMCeffBoIDYnktXCUHYEA1VKy6b7vMu+6O3DHFUKVFdQE0p8rUWaf/G62BIMMhMPMcjpGPf3yru9xwvU/whtTRnUa/7qBv1VHmbFoCRVCQwqNhCbpj8d57uff4ZgbUhrhylT7pr9s4OXaMHMX/wMSjaQUKIqCSCZBJnnq3u8y71urcccVohVeqkc/72K0nvJe1HgckVSQuidVCp782c3Mv2417oRCJK3zV13npFTtpQRFk6iJBM/e/W3mXZ/KK1Lpoyagt28MM/eUf0SVgriigKIgZBIpNNbf893UZ2pMIVLppUqvyebGOLMWvQunqoJQkWgk9Pjnfn4z868fx399mLmL/xGAuCZJxuM41CRP/eK7zPvWW3mN+tA/60uxD9944409K1euXDPe74RZh/RHBYX4B2CllPJD+s/XAkgpv5ct5tRTT5WbN28uSGf9+vWcccYZE7bZ9M7jqPcHiFd4aO0ewJtQONRYx6GWRt710nb+fP0FtP1yHQfP+yc+dP1PAXjl+KPp2NNJxOejpzV1SNsbTTBUVc2X/vc61p36IWZVph62duXWe/hzwElCOkhKB5WOJGHppk/W40FhkWuAF97zZVRN5aRnHkAgee1957P0mbvpT/oYpoo4bmaJEZCSZleMde84DVUNUVOz5IhHRPj9a+nsXMXw8Hbq64+jvX0FTU25T2dCaqHVQmb1hbY3Kyafuk9FXmb1V6H+rVpHM7wb1bFS7YNLG3D2R5CVLjRVxeF04ogoKDMrcD/1EhUVR8Pdd6O9+AK7ztnDvAv+hlpXgSug4hpKkqyFRJ2GO+TE9/wb8KlPEZvlYNuNGs3N/8LcudegNHgQYZV9bbOIuCtwC5Wj+nrwJVS6/3MJfctUTjjhl1S8egi+9jVCx/vwb3uNuL+KmM/DMQe6QUoONjfhnRFn7kt++OY34e9/Z8+FCYZP9SAvCtPadYiGQJBQdQX9TfV4YwqD9bUcg4faWnj5djeNjR/kj55LWHrFFZzwpz8yXFfFSG01Kk7qAmFa/H66m2cw+Ls5aO138J3ne7j9uutoXVDNthuSyIuCVPujzPYP4kkqHJgzE2dSo7+piU3vXcb5Tz5J+HNe+papjMz9BfXv/RjH79tPpNJHz6xGAJwxjVa/n6qOo3j5Dh/VTUv4qfObnHLzzXzylVc4GDyEU9GI+iqoDMdoHfAT9bp5Y8Fs6v/8fb66vZI7L7mE+Y2uUU8HP/0nTtq1F09SobuliZjXgzuWROKgsb4B+ZFqOj8RR8y9g5ue6+G+z30O0HizvQWABG46DnQhBLzxqw4c7Qv4Q+0PqLztNr745JOEPuel58Eh6gcjaDiY29dP1OdhsK6a4aYaLl5zPz+74AJOdCbZmuHpG+/+R5qHhuiZ1Ui4sgIVJ41DAepDIZIXnsob50epa7+JC3VPLQf20ddYT9znQcFNe28/1dEIexbMJvFAO5tbfsnBNWu49Le/RdM9qRdFaOobptU/iOJ20tk6E29MobtpBqK6jtNCQ+z4tjjM04V3riFU6WOgqQ4NB55YkqN6egnUVdK59hja2i7nI13Hj3raIhXqhsKEfVV4kgqaW+CNJhhuquVdL20vyT4shHhRSnnqeL+bimu85gCZD43pAk4b20gIcRFwEcCsWbNYv359QSKhUChnzIIePyM1lQiZunZBCkHc46K5x8/69esJRsIknQ5GgsHR9zr20DCqwwGCVBAQ87qZOTSC0BQ2vrCBWlLXZgQYQlCHn0ZmMoyqCTSpIYTESZLOZBXr169HQ0OgIEh9eHRSRQ0RHFTglCqQxKO337SpX8/+6XEcVQA3EAqFgGpS17nm12/JZBKXK//NodD2ZsXkU/epyMus/irUv1XraIZ3ozpWqn3Dpz7OsT/4BVJqSK8DwglQJPs+/UmGNnYCnam7qufPB2BWywY8gzEUnwO10Yl0SxxxiM7y8eKLL9J60kkkq4cJhQShUIy9e9fznngSXJLZjl40BEK6cbpUCKsEwmFCIcnGjRup2T7EMdEowVCS5p5hhmtUkjSgCYEDSY13hLqeKOvXr2d+Vxf10SihiEIopLK4Zz+D7jqkEEhAQxDxelnUtx+aFhCMJQmFkoRCXZzMHubGYniSClH9GjEnGi5UVIeDxmCAA6FGEttf5vxINTVSMhIOEQpJFvQMMVRTnRq/9TMTMa+btp5++hSFpKIQCCcJhSR9r2/jeL8fzSGQAlIDviDudeNNKIQTcUIhSTDRj5MuapJJookEDUMh+ptqAYlAIgUkXQ5m9Q6zbfvrzAofB0AwFhv1NK+nj6TTgVsV6Y8VYl4Prf1+4tU1REIhQqEkXa+/hjviw51MEvO6R3MCSDodVCYShMMxEv5h3vTv42hFIax7aukdZrimCk9cRQqQAmJeF809Q0RDIRJCEMrw1E0XteFI6vMu4wMv7nbjTqoMBEOEQgkObt9BNLyQOOBOJIl5XaQf1S0FqA4HTQMhdgbC7AnswplIEEkmieue5vcMEPRV6DmldGJeF3N7+nijo45wPJ66H80/TJd/HwsUBVcyScLtHM0JQHMIKiIJQqEoO3bsJMbcUU/N/gAjtZVI/R9SEPO6Rz/ri7U/5stUHPH6DPAhKeWF+s9fAJZKKS/LFmPGEa80uWbBRmLSR7AqHW9djBrRXDS7Yrzwni9Puv1YjPzlL6Wc8Mn2k21vVowZ3o3EmNVfhfq3ah3N8G5Ux2q1Dzx8E+LW23F1Bki21yKvumLcC+vTbT1X3ozmdiB9TkRMxaFoJG67PmtM5lG1NCKSRG2upOaFoaLEBJc2kOhP0udrQpFO3EJlVsyPp9mVVaPz1FaihxzEfJ7UBAeBL5agYqZG++bucWMKHb/N+oyYVF763MtyeZVIw4h/IxqZGNmHJzriNRUX13cBmbfftQHj7yUlJnLZeXiUJN5oAjSJN5rAoySJXHZeUWOumtOKgpOI5kLVJBHNhYKTq+aMv+J5oe2LgVUXQC2XRVOns3cjMVZdKNlITKk1as+9gZoXhtj44N+peWEo6wQq3Tb9yApnIKE/siL7pAsyni8WSSLV1HeHoiGvuqJoMfKqK6hSYsyL9XA03cyL9VClxCbUqFvx7zQnh6iOR9CkoDoeoTk5RN2Kf88aU+j4bdZnxGTyktKaeZnVX/n4N6KRSdHHIymlqV+kTm/uBeYDHuBl4ISJYpYsWSILZd26dXm1W//zm+UL71gk32yZKV94xyK5/uc3lyTmob1/lu96+udy9rpH5Lue/rl8aO+fi9o+k3y9lyPT2buU09v/dPYuZWn9jzx0owy8q15GZ7tk4F31cuShG4seMxmNSIsj75hCx2+zPiOM5rXfonmZ1V/5+jeiMRmAzTLbPCjbL0r5BZwN7ALeAL6Vq30pJ15pDh48WLCGGTFGNIwMwGbkZUaMGd6NxJjVX/Z2XxhW9WLXvjDsMa+0GkZipvt2P9HEa0oeoCqlfAx4bCq0s9HaWvhpPDNijGgYwYy8zIoxQ8Oq/WWGhlX7ywhW9WLXvvRYtY7lUvvp7D0X9pJBOj09PZaMMaJhBDPyMivGDA2r9pcZGlbtLyNY1Ytd+9Jj1TqWS+2ns/dc2BMvnZaWFkvGGNEwghl5mRVjhoZV+8sMDav2lxGs6sWufemxah3LpfbT2Xsu7ImXzqFDWR+cP6UxRjSMYEZeZsWYoWHV/jJDw6r9ZQSrerFrX3qsWsdyqf109p4Le+Kl06CvMWa1GCMaRjAjL7NizNCwan+ZoWHV/jKCVb3YtS89Vq1judR+OnvPhT3x0gkGg5aMMaJhBDPyMivGDA2r9pcZGlbtLyNY1Ytd+9Jj1TqWS+2ns/dc2BMvnUIXwDQrxoiGEczIy6wYMzSs2l9maFi1v4xgVS927UuPVetYLrWfzt5zYU+8dBRFsWSMEQ0jmJGXWTFmaFi1v8zQsGp/GcGqXuzalx6r1rFcaj+dvefCnnjpFLoOk1kxRjSMYEZeZsWYoWHV/jJDw6r9ZQSrerFrX3qsWsdyqf109p4LS0+8hBAfFUKsGRwcJBKJEAwGCQQCRKNRBgcHURSF/v5+pJSjz9no7k4t+6goClJK+vv7URSFwcFBotEogUCAYDBIJBJheHiYRCLBwMAAQojR9ZjS75H+3tfXRzKZxO/3E4vFGBkZIRwOE4/HGRkZIRaL4ff7SSaT9PX1jfsevb29aJrGyMgIiUSC4eHhvDw5nc7R9+jp6cnLk6ZpDAwMoGla3p4ikQjhcJhwOJyXJ6fTOeppYGAgL08Oh+OIOuXylH7fdJ1yeZJSHlGnXJ4y+zhfT5qmZd32xvOkKErWbS+bJ6fTmXXby7dOuTxFo9G89qdMT5qm5dyfxnrK7ON8PAWDwYI9aZpW0BjR09ODw+HIe4xIe8r0ko+nSCRScJ1UVc17jChkf8r0NDIyUtAYMTIyQjweL3jbE0LkPUak999CPamqmvcYkfaUTCYL2p8GBgYKGiMURWF4eLjgOqXH43zHPUVRCh4j0l4KGfdCoVBB214oFCpojMjMq5BxL300Kt9xLxaLFbw/DQ8PFzRG5EKknmxvbYQQh4A3CwybAQwU0L4OGClQw4wYIxqFejeiY9X+MsO7kRiz+sve7gvDql7s2heGPeaVVsNIzHTf7juklHXj/ibbWkJv9y8mWCcpS/s1BjRKHmNQoyDvJuZlRn+V3LvF+8ve7svDi117i9Xewv1lb/fWzCtrjKVPNZrMHywaY0TDCGbkZVaMGRpW7S8zNKzaX0awqhe79qXHqnUsl9pPZ+8TxrwtTjUaQQixWUp56lTnMRXY3qend5je/qezd5je/m3v09M7vD39l/MRrzVTncAUYnufvkxn/9PZO0xv/7b36cvbzn/ZHvGysbGxsbGxsbEa5XzEy8bGxsbGxsbGUpTdxEsI8WEhxE4hxB4hxP+b6nzMRgixXwjxqhBiqxBi81TnU0qEED8XQvQLIbZlvNYohPirEGK3/t2cFXdNJov3lUKIg3rttwohzp7KHEuJEKJdCLFOCLFdCPGaEOLr+utlX/8JvJd9/YUQPiHEC0KIl3XvN+qvzxdCbNTr/rAQwjPVuZaCCfzfI4TYl1H7xVOda6kQQjiFEFuEEH/Uf37b1b6sJl5CCCdwB7AcOB74rBDi+KnNako4U0q5+O12waEB7gE+POa1/wf8XUrZAfxd/7kcuYcjvQPcrtd+sZTyMZNzMpMkcJWU8jjgdOASfV+fDvXP5h3Kv/5xYJmU8hRgMfBhIcTpwC2kvHcAQ8BXpjDHUpLNP8CKjNpvnboUS87Xge0ZP7/tal9WEy9gKbBHSrlXSpkAHgI+NsU52ZQIKeVTwOCYlz8G/J/+//8DPm5qUiaRxfu0QUrZI6V8Sf9/kNRAPIdpUP8JvJc9MkVI/9Gtf0lgGfAr/fWyrDtM6H9aIIRoA84B7tJ/FrwNa19uE685QGfGz11MkwEpAwn8RQjxohDioqlOZgqYJaXsgdQHFNA8xfmYzaVCiFf0U5Fld5ptPIQQ84B3ABuZZvUf4x2mQf31U01bgX7gr8AbwLCUMqk3Ketxf6x/KWW69t/Ra3+7EMI7hSmWku8D1wCa/nMTb8Pal9vEa7yVLKfNXwM675ZSvpPU6dZLhBDvm+qEbEzjJ8DRpE5B9AC3Tm06pUcIUQ38GviGlDIw1fmYyTjep0X9pZSqlHIx0EbqLMdx4zUzNyvzGOtfCHEicC2wCHgX0Ah8cwpTLAlCiI8A/VLKFzNfHqep5WtfbhOvLqA94+c2oHuKcpkSpJTd+vd+4LekBqbpRJ8QYjaA/r1/ivMxDSllnz4oa8CdlHnthRBuUhOP+6WUv9Ffnhb1H8/7dKu/lHIYWE/qOrd6IYRL/9W0GPcz/H9YP/0spZRx4G7Ks/bvBv5ZCLGf1GVEy0gdAXvb1b7cJl6bgA79LgcP8K/Ao1Ock2kIIaqEEDXp/wP/BGybOKrseBT4ov7/LwK/n8JcTCU94dD5BGVce/3ajp8B26WUt2X8quzrn837dKi/EGKmEKJe/38FcBapa9zWAZ/Wm5Vl3SGr/x0Zf2wIUtc4lV3tpZTXSinbpJTzSH22PyGlPI+3Ye3L7gGq+i3U3wecwM+llN+Z4pRMQwixgNRRLgAX8EA5+xdCPAicQWp1+j7gP4HfAY8Ac4EDwGeklGV3EXoW72eQOs0kgf3Av6Wvdyo3hBDvAZ4GXuWt6z3+g9S1TmVd/wm8f5Yyr78Q4mRSF1A7SR04eERKeZM+9j1E6jTbFuDz+tGfsmIC/08AM0mdetsK/HvGRfhlhxDiDOBqKeVH3o61L7uJl42NjY2NjY2NVSm3U402NjY2NjY2NpbFnnjZ2NjY2NjY2JiEPfGysbGxsbGxsTEJe+JlY2NjY2NjY2MS9sTLxsbGxsbGxsYk7ImXjY0NAEIIKYS4NePnq4UQK4v03vcIIT6du+WkdT4jhNguhFhXYp39QogZpdTII4eVQoirC4wp20cM2Ni8XbAnXjY2NmniwCenekIxFiGEs4DmXwG+JqU8s4j6rtytbGxsbPLDnnjZ2NikSQJrgCvG/mLsEav0kRMhxBlCiCeFEI8IIXYJIf5LCHGeEOIFIcSrQoijM97mLCHE03q7j+jxTiHEKiHEJn2B33/LeN91QogHSD0odGw+n9Xff5sQ4hb9tRuA9wD/K4RYNU7MNXrMy0KI/9Jf+6qu/bIQ4tdCiMoMv7fpR85uEUI0CSH+IoTYIoT4KfoacfpqEX/S47cJIc4dR3e9EOIWvU92CSHeO5F3/XcrMl6/MeP1bwkhdgoh/gYszHj9aCHEn4UQL+p9vEh/fb4Q4nn9vW4em5uNjY352H/J2djYZHIH8IoQ4r8LiDmF1ELFg8Be4C4p5VIhxNeBy4Bv6O3mAe8ntZDzOiHEMcD5wIiU8l1CCC/wrBDiL3r7pcCJUsp9mWJCiFbgFmAJMAT8RQjxcf0J3stIPdF685iY5aSWUjlNShkRQjTqv/qNlPJOvc23SR0x+5H+u2OBs6SUqhDih8AzusY5wEV6mw8D3VLKc/T3qMvSRy69T84mtcrAWbrWeN479K+lpCZ4j4rUYvdhUkulvIPU2P0SkF4weA2pp5XvFkKcBvyY1Fp2PwB+IqW8VwhxSZbcbGxsTMSeeNnY2IwipQwIIe4FLgeieYZtSi9NI4R4A0hPnF4FMk/5PaIv4LxbCLEXWERqPdGTM46m1ZGadCSAF8ZOunTeBayXUh7SNe8H3kdquahsnAXcLaWM6D7TywidqE+46oFq4PGMmF9KKVX9/+8DPqnH/kkIMZTh8X/0o25/lFI+nUU/vYj3i6QmoEzg/Z/0ry3669X66zXAb9MehBCP6t+rgX8EfimESOt59e/vBj6l//8XpCasNjY2U4g98bKxsRnL90kdTbk747Uk+qUJIvXp7sn4Xea6aFrGzxqHjzFj1yeTpI7oXCalzJzwpNdiC2fJT2R5fSLEOPoA9wAfl1K+LIT4Eqn1LtOM1T8iXkq5SwixBDgb+J4Q4i9SypvG0Un3icpbfZLN+4eA70kpfzrm9W9k8eAAhqWUi8f53bh529jYTB32NV42NjaHoR8NeoTUqbA0+0md2gP4GOA28NafEUI49Ou+FgA7SR1hulgI4QYQQhwrhKjK8T4bgfcLIWboF95/FngyR8xfgAsyruFKn2qsAXp0/fMmiH8q/Xv9tGWD/v9WICKlvA/4H+CdOfLIJJv3x/Vcq/XX5wghmvUcPiGEqBBC1AAfhdRRSmCfEOIzenshhDhF13iW1OlJcvizsbExCfuIl42NzXjcClya8fOdwO+FEC8Afyf70aiJ2ElqgjSL1PVIMSHEXaROvb2kH0k7ROparKxIKXuEENcC60gdNXpMSvn7HDF/FkIsBjYLIRLAY8B/ANeTmsi9Seq0YU2Wt7gReFAI8ZLu4YD++knAKiGEBijAxRPlMYZxvUsp/yKEOA54Xj91GAI+L6V8SQjxMLBVzzfztOZ5wE+EENeRmhQ/BLwMfB14QL/e7tcF5GZjY1MihJT2UWgbGxsbGxsbGzOwTzXa2NjY2NjY2JiEPfGysbGxsbGxsTEJe+JlY2NjY2NjY2MS9sTLxsbGxsbGxsYk7ImXjY2NjY2NjY1J2BMvGxsbGxsbGxuTsCdeNjY2NjY2NjYmYU+8bGxsbGxsbGxM4v8DcdPqrvrIPS4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_what_firsts(counters, types=(3, 4, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I was quite surprised to see how closely the straight flush and four of a kind curves track each other; I can't explain why:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_what_firsts(counters, types=(7, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That concludes our exploration of poker ranking; I hope it has inspired you to explore some on your own."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
